/**
 * Minified by jsDelivr using UglifyJS v3.3.25.
 * Original file: /npm/jquery.event.move@1.3.6/js/jquery.event.move.js
 * 
 * Do NOT use SRI with dynamically generated files! More information: https://www.jsdelivr.com/using-sri-with-dynamic-files
 */
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//# sourceMappingURL=/sm/f7f1ac2dc7bb806b0a27e162f37dd0b18f107228974dd11fd328132c5c944a16.map(function( factory ) {

	if ( typeof define === "function" && define.amd ) {

		define( ["jquery", "../jquery.validate"], factory );

	} else if (typeof module === "object" && module.exports) {

		module.exports = factory( require( "jquery" ) );

	} else {

		factory( jQuery );

	}

}(function( $ ) {



/*

 * Translated default messages for the jQuery validation plugin.

 * Locale: PT (Portuguese; português)

 * Region: BR (Brazil)

 */

$.extend( $.validator.messages, {



	// Core

	required: "Este campo &eacute; requerido.",

	remote: "Por favor, corrija este campo.",

	email: "Por favor, forne&ccedil;a um endere&ccedil;o de email v&aacute;lido.",

	url: "Por favor, forne&ccedil;a uma URL v&aacute;lida.",

	date: "Por favor, forne&ccedil;a uma data v&aacute;lida.",

	dateISO: "Por favor, forne&ccedil;a uma data v&aacute;lida (ISO).",

	number: "Por favor, forne&ccedil;a um n&uacute;mero v&aacute;lido.",

	digits: "Por favor, forne&ccedil;a somente d&iacute;gitos.",

	creditcard: "Por favor, forne&ccedil;a um cart&atilde;o de cr&eacute;dito v&aacute;lido.",

	equalTo: "Por favor, forne&ccedil;a o mesmo valor novamente.",

	maxlength: $.validator.format( "Por favor, forne&ccedil;a n&atilde;o mais que {0} caracteres." ),

	minlength: $.validator.format( "Por favor, forne&ccedil;a ao menos {0} caracteres." ),

	rangelength: $.validator.format( "Por favor, forne&ccedil;a um valor entre {0} e {1} caracteres de comprimento." ),

	range: $.validator.format( "Por favor, forne&ccedil;a um valor entre {0} e {1}." ),

	max: $.validator.format( "Por favor, forne&ccedil;a um valor menor ou igual a {0}." ),

	min: $.validator.format( "Por favor, forne&ccedil;a um valor maior ou igual a {0}." ),

	step: $.validator.format( "Por favor, forne&ccedil;a um valor m&uacute;ltiplo de {0}." ),



	// Metodos Adicionais

	maxWords: $.validator.format( "Por favor, forne&ccedil;a com {0} palavras ou menos." ),

	minWords: $.validator.format( "Por favor, forne&ccedil;a pelo menos {0} palavras." ),

	rangeWords: $.validator.format( "Por favor, forne&ccedil;a entre {0} e {1} palavras." ),

	accept: "Por favor, forne&ccedil;a um tipo v&aacute;lido.",

	alphanumeric: "Por favor, forne&ccedil;a somente com letras, n&uacute;meros e sublinhados.",

	bankaccountNL: "Por favor, forne&ccedil;a com um n&uacute;mero de conta banc&aacute;ria v&aacute;lida.",

	bankorgiroaccountNL: "Por favor, forne&ccedil;a um banco v&aacute;lido ou n&uacute;mero de conta.",

	bic: "Por favor, forne&ccedil;a um c&oacute;digo BIC v&aacute;lido.",

	cifES: "Por favor, forne&ccedil;a um c&oacute;digo CIF v&aacute;lido.",

	creditcardtypes: "Por favor, forne&ccedil;a um n&uacute;mero de cart&atilde;o de cr&eacute;dito v&aacute;lido.",

	currency: "Por favor, forne&ccedil;a uma moeda v&aacute;lida.",

	dateFA: "Por favor, forne&ccedil;a uma data correta.",

	dateITA: "Por favor, forne&ccedil;a uma data correta.",

	dateNL: "Por favor, forne&ccedil;a uma data correta.",

	extension: "Por favor, forne&ccedil;a um valor com uma extens&atilde;o v&aacute;lida.",

	giroaccountNL: "Por favor, forne&ccedil;a um n&uacute;mero de conta corrente v&aacute;lido.",

	iban: "Por favor, forne&ccedil;a um c&oacute;digo IBAN v&aacute;lido.",

	integer: "Por favor, forne&ccedil;a um n&uacute;mero n&atilde;o decimal.",

	ipv4: "Por favor, forne&ccedil;a um IPv4 v&aacute;lido.",

	ipv6: "Por favor, forne&ccedil;a um IPv6 v&aacute;lido.",

	lettersonly: "Por favor, forne&ccedil;a apenas com letras.",

	letterswithbasicpunc: "Por favor, forne&ccedil;a apenas letras ou pontua&ccedil;ões.",

	mobileNL: "Por favor, fornece&ccedil;a um n&uacute;mero v&aacute;lido de telefone.",

	mobileUK: "Por favor, fornece&ccedil;a um n&uacute;mero v&aacute;lido de telefone.",

	nieES: "Por favor, forne&ccedil;a um NIE v&aacute;lido.",

	nifES: "Por favor, forne&ccedil;a um NIF v&aacute;lido.",

	nowhitespace: "Por favor, n&atilde;o utilize espa&ccedil;os em branco.",

	pattern: "O formato fornecido &eacute; inv&aacute;lido.",

	phoneNL: "Por favor, forne&ccedil;a um n&uacute;mero de telefone v&aacute;lido.",

	phoneUK: "Por favor, forne&ccedil;a um n&uacute;mero de telefone v&aacute;lido.",

	phoneUS: "Por favor, forne&ccedil;a um n&uacute;mero de telefone v&aacute;lido.",

	phonesUK: "Por favor, forne&ccedil;a um n&uacute;mero de telefone v&aacute;lido.",

	postalCodeCA: "Por favor, forne&ccedil;a um n&uacute;mero de c&oacute;digo postal v&aacute;lido.",

	postalcodeIT: "Por favor, forne&ccedil;a um n&uacute;mero de c&oacute;digo postal v&aacute;lido.",

	postalcodeNL: "Por favor, forne&ccedil;a um n&uacute;mero de c&oacute;digo postal v&aacute;lido.",

	postcodeUK: "Por favor, forne&ccedil;a um n&uacute;mero de c&oacute;digo postal v&aacute;lido.",

	postalcodeBR: "Por favor, forne&ccedil;a um CEP v&aacute;lido.",

	require_from_group: $.validator.format( "Por favor, forne&ccedil;a pelo menos {0} destes campos." ),

	skip_or_fill_minimum: $.validator.format( "Por favor, optar entre ignorar esses campos ou preencher pelo menos {0} deles." ),

	stateUS: "Por favor, forne&ccedil;a um estado v&aacute;lido.",

	strippedminlength: $.validator.format( "Por favor, forne&ccedil;a pelo menos {0} caracteres." ),

	time: "Por favor, forne&ccedil;a um hor&aacute;rio v&aacute;lido, no intervado de 00:00 a 23:59.",

	time12h: "Por favor, forne&ccedil;a um hor&aacute;rio v&aacute;lido, no intervado de 01:00 a 12:59 am/pm.",

	url2: "Por favor, forne&ccedil;a uma URL v&aacute;lida.",

	vinUS: "O n&uacute;mero de identifica&ccedil;&atilde;o de ve&iacute;culo informado (VIN) &eacute; inv&aacute;lido.",

	zipcodeUS: "Por favor, forne&ccedil;a um c&oacute;digo postal americano v&aacute;lido.",

	ziprange: "O c&oacute;digo postal deve estar entre 902xx-xxxx e 905xx-xxxx",

	cpfBR: "Por favor, forne&ccedil;a um CPF v&aacute;lido.",

	nisBR: "Por favor, forne&ccedil;a um NIS/PIS v&aacute;lido",

	cnhBR: "Por favor, forne&ccedil;a um CNH v&aacute;lido.",

	cnpjBR: "Por favor, forne&ccedil;a um CNPJ v&aacute;lido."

} );

return $;

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	<title>Artifical Intelligence &#8211; Qube Infrastructures</title>
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	<title>Artifical Intelligence &#8211; Qube Infrastructures</title>
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		<title>How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit NLTK</title>
		<link>https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13451</link>
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		<dc:creator><![CDATA[prith001]]></dc:creator>
		<pubDate>Thu, 18 Jul 2024 14:54:01 +0000</pubDate>
				<category><![CDATA[Artifical Intelligence]]></category>
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					<description><![CDATA[Getting Started with Sentiment Analysis using Python Now, we will choose the best parameters obtained from GridSearchCV and create a final random forest classifier model and then train our new model. As the data is in text format, separated by semicolons and without column names, we will create the data frame with read_csv() and parameters &#8230;<p class="read-more"> <a class="" href="https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13451"> <span class="screen-reader-text">How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit NLTK</span> Read More &#187;</a></p>]]></description>
										<content:encoded><![CDATA[<p><h1>Getting Started with Sentiment Analysis using Python</h1>
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" width="301px" alt="sentiment analysis in nlp"/></p>
<p><p>Now, we will choose the best parameters obtained from GridSearchCV and create a final random forest classifier model and then train our new model. As the data is in text format, separated by semicolons and without column names, we will create the data frame with read_csv() and parameters as “delimiter” and “names”. Smart assistants such as Google&#8217;s Alexa use voice recognition to understand everyday phrases and inquiries. They then use a subfield of NLP called natural language generation (to be discussed later) to respond to queries.</p>
</p>
<p><p>NLP enables computers to understand human languages by breaking down text into smaller components such as words and phrases and analyzing their meanings. Logistic regression is a statistical method used for binary classification, which means it’s designed to predict the probability of a categorical outcome with two possible values. It can be challenging for computers to understand human language completely. They struggle with interpreting sarcasm, idiomatic expressions, and implied sentiments.</p>
</p>
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" width="308px" alt="sentiment analysis in nlp"/></p>
<p><p>We will find the probability of the class using the predict_proba() method of Random Forest Classifier and then we will plot the roc curve. We will evaluate our model using various metrics such as Accuracy Score, Precision Score, Recall Score, Confusion Matrix and create a roc curve to visualize how our model performed. Now, we will convert the text data into vectors, by fitting and transforming the corpus that we have created. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. And then, we can view all the models and their respective parameters, mean test score and rank, as GridSearchCV stores all the intermediate results in the cv_results_ attribute.</p>
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<p><p>For a beginner to NLP, looking at these tasks and all the techniques involved in handling such tasks can be quite daunting. And in fact, it is very difficult for a newbie to know exactly where and how to start. This includes gathering data from reliable sources such as FAQs or product manuals that can be used to train the bot’s responses. Sentiment analysis is essential for businesses to gauge customer response.</p>
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<p><p>Convin’s products and services offer a comprehensive solution for call centers looking to implement NLP-enabled sentiment analysis. Sentiment analysis, also known as sentimental analysis, is the process of determining and understanding the emotional tone and attitude conveyed within text data. It involves assessing whether a piece of text expresses positive, negative, neutral, or other sentiment categories.</p>
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<p><h2>Sentiment Analysis — Intro and Implementation</h2>
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<p><p>ABSA can help organizations better understand how their products are succeeding or falling short of customer expectations. Over here, the lexicon method, tokenization, and parsing come in the rule-based. The approach is that counts the number of positive and negative words in the given dataset.</p>
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<p><p>Traditionally, computers were only able to understand structured data such as numbers or symbols. However, with advancements in technology, NLP has made it possible for machines to comprehend and analyze unstructured data like text, speech, and images. This has opened up a wide range of possibilities for applications in various industries such as healthcare, finance, customer service, marketing, and more. Customers usually talk about products on social media and customer feedback forums. This data can be collected and analyzed to gauge overall customer response. In order to gauge customer’s response to this product, sentiment analysis can be performed.</p>
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<div style='border: grey solid 1px;padding: 14px;'>
<h3>Sentiment Analysis: How To Gauge Customer Sentiment (2024) &#8211; Shopify</h3>
<p>Sentiment Analysis: How To Gauge Customer Sentiment ( .</p>
<p>Posted: Thu, 11 Apr 2024 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiW0FVX3lxTE5wY2dYNHUzXzJVenNUU1dBS0gzMkIyVkI5R01DM2d1LTQ5QmcyMGFtRk52TFVsMmc0VDdKMjNRRnRoMmpVTkVXNUtMM1d2WHBBUVBTM3VLWDhfcXM?oc=5' rel="nofollow">source</a>]</p>
</div>
<p><p>It has gained significant attention in recent years due to its wide range of applications in various industries such as marketing, customer service, and social media monitoring. To solve this problem, we will follow the typical machine learning pipeline. We will then do exploratory data analysis to see if we can find any trends in the dataset. Next, we will perform text preprocessing to convert textual data to numeric data that can be used by a machine learning algorithm. Finally, we will use machine learning algorithms to train and test our sentiment analysis models. Sentiment analysis focuses on determining the emotional tone expressed in a piece of text.</p>
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<p><p>However, adding new rules may affect previous results, and the whole system can get very complex. Since rule-based systems often require fine-tuning and maintenance, they’ll also need regular investments. If Chewy wanted to unpack the what and why behind their reviews, in order to further improve their services, they would need to analyze each and every negative review at a granular level. The juice brand responded to a viral video that featured someone skateboarding while drinking their cranberry juice and listening to Fleetwood Mac. In addition to supervised models, NLP is assisted by unsupervised techniques that help cluster and group topics and language usage. This model uses convolutional neural network (CNN) absed approach instead of conventional NLP/RNN method.</p>
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<p><p>The answer lies in deep learning – a subset of AI that involves training neural networks on large datasets to recognize patterns and make predictions based on new information. Rule-based approaches rely on predefined sets of rules, patterns, and lexicons to determine sentiment. These rules might include lists of positive and negative words or phrases, grammatical structures, and emoticons. Rule-based methods are relatively simple and interpretable but may lack the flexibility to capture nuanced sentiments.</p>
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<p><p>You will use the Naive Bayes classifier in NLTK to perform the modeling exercise. Notice that the model requires not just a list of words in a tweet, but a Python dictionary with words as keys and True as values. The following function makes a generator function to change the format of the cleaned data. This time, you also add words from the names corpus to the unwanted list on line 2 since movie reviews are likely to have lots of actor names, which shouldn’t be part of your feature sets.</p>
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<p><p>Machine learning applies algorithms that train systems on massive amounts of data in order to take some action based on what’s been taught and learned. Here, the system learns to identify information based on patterns, keywords and sequences rather than any understanding of what it means. Social media listening with sentiment analysis allows businesses and organizations to monitor and react to emerging negative sentiments before they cause reputational damage. This helps businesses and other organizations understand opinions and sentiments toward specific topics, events, brands, individuals, or other entities. Similarly, in customer service, opinion mining is used to analyze customer feedback and complaints, identify the root causes of issues, and improve customer satisfaction.</p>
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<p><h2>What are the Approaches to Sentiment Analysis?</h2>
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<p><p>In addition to the different approaches used to build sentiment analysis tools, there  are also different types of sentiment analysis that organizations turn to depending on their needs. By default, the data contains all positive tweets followed by all negative tweets in sequence. When training the model, you should provide a sample of your data that does not contain any bias. To avoid bias, you’ve added code to randomly arrange the data using the .shuffle() method of random.</p>
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<p><p>You can exclude all other columns from the dataset except the ‘text’ column. The Machine Learning Algorithms usually expect features in the form of numeric vectors. Sentiment analysis (SA) or opinion mining is a general dialogue preparation chore that intends to discover sentiments behind the opinions in texts on changeable subjects.</p>
</p>
<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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" width="305px" alt="sentiment analysis in nlp"/></p>
<p><p>And, because of this upgrade, when any company promotes their products on Facebook, they receive more specific reviews which in turn helps them to enhance the customer experience. In this section, we look at how to load and perform predictions on the trained model. We can change the interval of evaluation by changing the logging_steps argument in TrainingArguments. In addition to the default training and validation loss metrics, we also get additional metrics which we had defined in the compute_metric function earlier. Reliable monitoring for your app, databases, infrastructure, and the vendors they rely on.</p>
</p>
<p><p>This allows them to capture complex patterns and relationships between words and phrases, making them ideal for sentiment analysis tasks. For example, if a customer expresses a negative opinion along with a positive opinion in a review, a human assessing the review might label it negative before reaching the positive words. AI-enhanced sentiment classification helps sort and classify text in an objective manner, so this doesn’t happen, and both sentiments are reflected.</p>
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<p><p>Note that the index of the column will be 10 since pandas columns follow zero-based indexing scheme where the first column is called 0th column. Our label set will consist of the sentiment of the tweet that we have to predict. To create a feature and a label set, we can use the iloc method off the pandas data frame. Opinions expressed on social media, whether true or not, can destroy a brand reputation that took years to build. Robust, AI-enhanced sentiment analysis tools help executives monitor the overall sentiment surrounding their brand so they can spot potential problems and address them swiftly.</p>
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<p><p>The potential applications of sentiment analysis are vast and continue to grow with advancements in AI and machine learning technologies. Natural Language Processing (NLP) is the area of machine learning that focuses on the generation and understanding of language. Its main objective is to enable machines to understand, communicate and interact with humans in a natural way. The role of chatbots in NLP lies in their ability to understand and respond to natural language input from users. This means that rather than relying on specific commands or keywords like traditional computer programs, chatbots can process human-like questions and responses.</p>
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<p><h2>Problems, use-cases, and methods: from simple to advanced</h2>
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<p><p>Join us on this exciting journey as we unravel the applications of Deep Learning in NLP and uncover its potential to reshape our digital landscape. If you do not have access to a GPU, you are better off with iterating through the dataset using predict_proba. We will iterate through 10k samples for predict_proba make a single prediction at a time while scoring all 10k without iteration using the batch_predict_proa method. Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. Similarly, max_df specifies that only use those words that occur in a maximum of 80% of the documents.</p>
</p>
<p><p>A frequency distribution is essentially a table that tells you how many times each word appears within a given text. In NLTK, frequency distributions are a specific object type implemented as a distinct class called FreqDist. Data Scientist with 6 years of experience in analysing large datasets and delivering valuable insights via advanced data-driven methods. Proficient in Time Series Forecasting, Natural Language Processing and with a demonstrated history of working in the Telecom, Healthcare and Retail Supply Chain industries.</p>
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<p><p>In the age of social media, a single viral review can burn down an entire brand. On the other hand,&nbsp;research by Bain &amp; Co.&nbsp;shows that good experiences can grow 4-8% revenue over competition by increasing customer lifecycle 6-14x and improving retention up to 55%. Of course, not every sentiment-bearing phrase takes an adjective-noun form.</p>
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<p><p>VADER is particularly effective for analyzing sentiment in social media text due to its ability to handle complex language such as sarcasm, irony, and slang. It also provides a sentiment intensity score, which indicates the strength of the sentiment expressed in the text. Python is a popular programming language for natural language processing (NLP) tasks, including sentiment analysis. Sentiment analysis is the process of determining the emotional tone behind a text. There are considerable Python libraries available for sentiment analysis, but in this article, we will discuss the top Python sentiment analysis libraries.</p>
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<p><p>A basic way of breaking language into tokens is by splitting the text based on whitespace and punctuation. With these classifiers imported, you’ll first have to instantiate each one. Thankfully, all of these have pretty good defaults and don’t require much tweaking. After you’ve installed scikit-learn, you’ll be able to use its classifiers directly within NLTK. Feature engineering is a big part of improving the accuracy of a given algorithm, but it’s not the whole story.</p>
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<p><p>Using different libraries, developers can execute machine learning algorithms to analyze large amounts of text. Hybrid approaches combine elements of both rule-based and machine learning methods to improve accuracy and handle diverse types of text data effectively. For example, a rule-based system could be used to preprocess data and identify explicit sentiment cues, which are then fed into a machine learning model for fine-grained sentiment analysis. In today’s data-driven world, understanding and interpreting the sentiment of text data is a crucial task.</p>
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<div style='border: black dashed 1px;padding: 12px;'>
<h3>10 Best Python Libraries for Sentiment Analysis (2024) &#8211; Unite.AI</h3>
<p>10 Best Python Libraries for Sentiment Analysis ( .</p>
<p>Posted: Tue, 16 Jan 2024 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMieEFVX3lxTE56Q201Um9xU0NZb1E2dEZvc3VkTlZYNFVGZlNnNTgwaGhaRGlfZng2MllZN0dqa214U1lZeTFuV0Ewems1TWhxTUJzS29EMVVlQTVmTnN2X0tkS1A1MmZldllweFZKU3lqOEYwVU91b0ZMOWQ1M3pNYQ?oc=5' rel="nofollow">source</a>]</p>
</div>
<p><p>In this section, you’ll learn how to integrate them within NLTK to classify linguistic data. Since you’re shuffling the feature list, each run will give you <a href="https://chat.openai.com/">https://chat.openai.com/</a> different results. In fact, it’s important to shuffle the list to avoid accidentally grouping similarly classified reviews in the first quarter of the list.</p>
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<p><p>Sentiment Analysis is a sub-field of NLP and together with the help of machine learning techniques, it tries to identify and extract the insights from the data. Hence, it becomes very difficult for machine learning models to figure out the sentiment. Here are the probabilities projected on a horizontal bar chart for each of our test cases. Notice that the positive and negative test cases have a high or low probability, respectively. The neutral test case is in the middle of the probability distribution, so we can use the probabilities to define a tolerance interval to classify neutral sentiments. There are various types of NLP models, each with its approach and complexity, including rule-based, machine learning, deep learning, and language models.</p>
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<p><p>As we humans communicate with each other in a Natural Language, which is easy for us to interpret but it’s much more complicated and messy if we really look into it. The id2label and label2id dictionaries has been incorporated into the configuration. We can retrieve these dictionaries from the model&#8217;s configuration during inference to find out the corresponding class labels for the predicted class ids.</p>
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<p><p>Ping Bot is a powerful uptime and performance monitoring tool that helps notify you and resolve issues before they affect your customers. Enough of the exploratory data analysis, our next step is to perform some preprocessing on the data and then convert the numeric data into text data as shown below. There are many sources of public sentiment e.g. public interviews, opinion polls, surveys, etc. However, with more and more people joining social media platforms, websites like Facebook and Twitter can be parsed for public sentiment. Sentiment analysis refers to analyzing an opinion or feelings about something using data like text or images, regarding almost anything. For instance, if public sentiment towards a product is not so good, a company may try to modify the product or stop the production altogether in order to avoid any losses.</p>
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<p><p>Now, we will use the Bag of Words Model(BOW), which is used to represent the text in the form of a bag of words ,i.e. The grammar and the order of words in a sentence are not given any importance, instead, multiplicity, i.e. (the number of times a word occurs in a document) is the main point of concern. It is a data visualization technique used to depict text in such a way that, the more frequent words appear enlarged as compared to less frequent words. This gives us a little insight into, how the data looks after being processed through all the steps until now.</p>
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<p><p>The field of natural language processing (NLP) has been revolutionized by the emergence of deep learning techniques. These methods, inspired by the way our brains process information, have shown remarkable success in applications such as sentiment analysis and chatbots. As we continue to make advancements in deep learning, it is important to explore its future potential in NLP and identify potential areas for growth. The&nbsp;overall sentiment&nbsp;is often inferred as&nbsp;positive,&nbsp;neutral&nbsp;or&nbsp;negative&nbsp;from the sign of the polarity score. Python is a valuable tool for natural language processing and sentiment analysis.</p>
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<ul>
<li>AutoNLP will automatically fine-tune various pre-trained models with your data, take care of the hyperparameter tuning and find the best model for your use case.</li>
<li>Depending on the requirement of your analysis, all of these versions may need to be converted to the same form, “run”.</li>
<li>One of the most significant advantages of combining NLP with deep learning is its ability to handle language variations such as slang words or typos.</li>
<li>Now, we will create a Sentiment Analysis Model, but it’s easier said than done.</li>
<li>For example, the words “social media” together has a different meaning than the words “social” and “media” separately.</li>
</ul>
<p><p>The regular expression re.sub(r&#8217;\W&#8217;, &#8216; &#8216;, str(features[sentence])) does that. From the output, you can see that the confidence level for negative tweets is higher compared to positive and neutral tweets. From the output, you can see that the majority <a href="https://www.metadialog.com/blog/sentiment-analysis-and-nlp/">sentiment analysis in nlp</a> of the tweets are negative (63%), followed by neutral tweets (21%), and then the positive tweets (16%). So how can we alter the logic, so you would only need to do all then training part only once &#8211; as it takes a lot of time and resources.</p>
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<p><p>RNNs are specialized neural networks for processing sequential data such as text or speech. Sentiment analysis has multiple applications, including understanding customer opinions, analyzing public sentiment, identifying trends, assessing financial news, and analyzing feedback. Before analyzing the text, some preprocessing steps usually need to be performed.</p>
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<p><p>Multilingual consists of different languages where the classification needs to be done as positive, negative, and neutral. Use the .train() method to train the model and the .accuracy() method to test the model on the testing data. Add the following code to convert the tweets from a list of cleaned tokens to dictionaries with keys as the tokens and True as values. The corresponding dictionaries are stored in positive_tokens_for_model and negative_tokens_for_model. Noise is specific to each project, so what constitutes noise in one project may not be in a different project.</p>
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<p><p>It is more complex than either fine-grained or ABSA and is typically used to gain a deeper understanding of a person’s motivation or emotional state. Rather than using polarities, like positive, negative or neutral, emotional detection can identify specific emotions in a body of text such as frustration, indifference, restlessness and shock. A company launching a new line of organic skincare products needed to gauge consumer opinion before a major marketing <a href="https://play.google.com/store/apps/datasafety?id=pl.edu.pg.chatpg&amp;hl=cs&amp;gl=US">Chat GPT</a> campaign. To understand the potential market and identify areas for improvement, they employed sentiment analysis on social media conversations and online reviews mentioning the products. The .train() and .accuracy() methods should receive different portions of the same list of features. Once you’re left with unique positive and negative words in each frequency distribution object, you can finally build sets from the most common words in each distribution.</p>
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<p><h2>Recommenders and Search Tools</h2>
</p>
<p><p>Book a demo with us to learn more about how we tailor our services to your needs and help you take advantage of all these tips &amp; tricks. For a more in-depth description of this approach, I recommend the interesting and useful paper Deep Learning for Aspect-based Sentiment Analysis by Bo Wanf and Min Liu from Stanford University. We’ll go through each topic and try to understand how the described problems affect sentiment classifier quality and which technologies can be  used to solve them.</p>
</p>
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J2mflt108Q3XTx010WXMEOuhwdZHX3pBcBQw9Y3/AK+/YNd39ru1+1UwvI311VsZ0/dS4vg8ku1lb29O7jPe35GxTAeLqTHOFKDE1G3oxVx/pYt27opRyezXx0IPPvVfP2nRYA4RbvUT2XENie8uhpKinqotTrtMrHtcPu/RNP4lZ0us5prbVTg6FkLtPv05f3leLdo6NPRa1wor3YZa+XH8Hp+i15ahb0pS4ySX1zgs3El5fcap0MTz1aFxDWj9p3i7/go6w8SAlz/flKLXT/RbZXUArA93S9cazc4d+3bu1Z3a6jXXRZxPdz5qGtHlbiaLjCld9H1DqJt3kxD1roj0fQPJkB3f7btn3hfLULiOtXNxdXzy2m1/XyPWY0fU6UKdNEyXEtGrhyGqwfl3xHNxxnJeMuH2uCChj6xHa6lr3GSaSAneH+HNrXOGncG6LOPLu0Op111UNci8rcS2nibuUlwt1TBS4blrKl872EMkEzXsiDT3Hc2TUEeDT8tFgaXb29WjWlV+JLd/vsZNWclKKRO7CV6kgmFrnc50Up1iJP6rvl9x/wAVefPxWKmSOie2VriHMIIPyWUYntljbO3ulaHj8QvW/Rrqsr21qWdXf3eNlvozj+0dqrerGpT3bX5O6Ii9OOZCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCo2IblU0Yigpzs6TUl3j+CrK8VytVPc2NErix7D8LgO77PtUrdvZZrwnOm1B4ZgLieprniHKG6s6V0nUZoa0NI8GP0P8Ac4lQZB1A0Go8Dqtptbgi33Kjnt1e/rFLVROhmhfHq2Rjho5p59xBIWvPOvKC9ZQYsqLTWRTzWmocZbZW7NWzxa/q7u4yN5BzfuPc4a6rVKWZKpHee5+hvW4U7eppN08TztRzzXNfPJHc5b3KbGhulQ6F1udUdaJLtS8a7ujLT9v9yu3FtiGJLDUW1jI+mOjoXvA+F4+XLlrzH3FUzEFLjW+V76K1VDLVb4DtNQ5x6SoOneNOYaNfs/8A0qYzAeNKQioosayGZpPJ5fof8Qf3K25uo4ylJJrkdQrWlp3rNpZ2FSpTqye3Lcs56ZeWlyKnl3hmvwzb6mO5uY2aplDhG1+4Ma0aDn3a8/8ABZVysslViLMjDdqo27nOudPLJoObYo3iSQ6+GjGOP7lYlhqL1Lby2/0scNZC4xvdGf0cmg/XafAHXxU2eE7IG4WqhdmRixj6Ssr4uit1K+LSSOA8zK4H9Uv0Gg/q9/ercISuLjMvmZWqajZdlOy7p22VmLjCMviblnivDLyZ3N5uZJJrJNSfsUXc+KGphzFq7jMC5twhinDzpzIYIz+OrFLj81Yz/rsn8sKws4MmDjLDxntMm+72/dLSt2hvTDT4oiSdBroCCe4geBK9A7N6lT069Uqj92Sw/DJ8a6vp11d273NtbyFWKb4cPWiS4/Qdxu4DmsNLQwdLK4E6a7fEDxVm/wCWap/9FuNfSXhZOqqaot9TJSVtNJDNC4sljlYWOY4d4IPMfiF8jzOup1PevVqtCtcSVShU2U10TOKpVKdFOFaGX82jG7c45pS2N+VuNdC4cjan6Dn3rJDdS3c5juf7JGmn/X/FNCO481X8FYKvWPL9BYrHA9znu/TTFpMdOzxe/TwHgO8nl4pDbsITq3VTKS4tJY8hPZupRp0KeG/Fv8mY+Gujr7VYrteY5ZIhcJ44W6D9ZsQcdf3yOH4FZfnuVdUQyU76p5ZIwtLXac+R/wCS9Vgy4tuGrPS2Ogq5OipIxHq5g1cfFxI8SdSftKqH5qRc9at5B/sf814n2hr09Zq1n+meV9MYPQtLs7qwjTx+nH8mKsc4jlwhg684ohoetPtdFLVNg3FvSFrdQ3XTULF2Bs487cXYVosV0OQM+IbVWNLmVlgu8UwDhyc10B1exw8Wk+P3EyAxdhaJlPNTVUIqrfWROina5o2kOGjmn5A6lQaxZgDPrhpv1Xf8nsRX2Ow1UnSNnoP0vRt7wypgcC1xA0G9zNp+w6gfNWn6VY2N3V0/VYvKe554rw6nsVS6q3FCNe1fmZ9GbOZjnmFnC5meZO4brYWsJ/2tNB9/NW3mPntnFl/YocQ4gyMbhu3S1DIYje73GZ6lxI1ZFTxgP3Aakk8h3/LXDUnHJxXVcRtkOMgKjTbvisVL02v2AxEf7quHK/J/NjObF1DmPnrdbxUUFHI2aGO6SHpqstO4MbEdOii17/hGo5AafENreab2dsaMqkIvOOb/AMzHo19QrTSnjBLignNdSU9UGdEaiOOTYeZG5uun96uZl2uMTWtjq3s2jQDlyA5Af4Lrh7DTq9hqpyYY/wDuxt5k/MD5Kt/mrDuDnVbyB4bAup9G+kVLK2qXlZYdRrZT44X++xzfayvUuqsKVB/Dx+f9FStdVJWUMVRKBvcNDp3ahepfKmp4qWBlPC3RjBoF9V6SzSU01FKXEIiIVhERAEREAREQBERAEREAREQBERAEREAREQBERAEREAXLQDrquFyNfDVYt5TnWouMHhlyjKMKmWdy3loFRcW4Mw5jqxz4exVaYLhRVA+Jko5td4OY4c2OHgQQR4Kr7j8j+5Nx+R/cuf8AZ11wz9zaU7pUZKpTbTXNcSJWMOBISTPnwJjHq8epLKW5xF+n2dKzmPvLSVaFv4Fs0JakR3TEmG6WAn+khlnmfp9jTGwf7ynJuOmhHIdyA+OiuqwvN3DzOwo+kjWaNPuu+z84pvzwYJyu4QcBYDr4L5faiTEVzpyHwuqWBsEb/wCsIuYJHgXE6eACztsa3RreQ7tPkgOndy1TcSr9pYXVOrtzeEjldV1qvq03VuqjnLlnkdw0aLnl4hfPcRyTeVjz029lJtP7mFG4opJMsnMHJrBOYu2outG6muDG7WV1MdkpHyfyIeB4bgSPAjUrEFfwhXJsp+icbUroieXWaRzXAf8AlcQf3BSV3kdybyeZ8V0Onax2h0uHd0Kvu9G0/wAmqvdJ0q/nt1ob+q3fgj1Y+EWmjlbLiPGD54wecNHT9GSP9pxP+CzdhTBuGsEWptow3a4qSAc3kc3yuPe57jzcfx0HcNAAFV95TeVj6pfa9rHu3VXMeieF5Iu2Wn6bp++hDD68WdmtC52hdA4+CbitJ7Nvev3Nn6zROZIYpY3RSxNe13eHAEH8CrarcEUsr99FUug5/qO1e0D7PFXJuKbj3rW3vZd6gv8A0QUn1zv+jMijqUbd5pywWO3Ll4n6VvUWnX+kEXxa/uVZt2DaSmcH1kxqXtOoBGjRz1+//rTu5Kv7jrr4puKwKHYWjRmp92m/F5/Jeqa1Ootly+x2axrW7WtDR9i5IXTeU3ldDHTLyOP5MF3NHicEaHREJ15oukpRlCCjLika2bUpNoIiK4UhERAEREAREQBERAEREAREQBERAEREAREQBERAEREAXZq6oDosa7pSr0ZU48S5Smqc05LKO6BddxTcVofZFzykvMz/AFyn0OX6a8l1QnVFv7WlKjSUJPejAqyU5ZQREWQWwiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIukssUEb5p5GxxxtLnvedGtA5kknkOStm05q5YX6ay09jzGwxcJcSCodZmUt2p5XXIQFwnNOGvPTdGWP37Ndux2umhTHMF0orWv2amWGFb9S4WxPmPhe0XquLRTW6vu9PT1M246N2xPeHHU8hoOfhqrpHPuOv3JghNPcgipeJsV4WwVaJsQ4yxNabBaqdzWzV91roqSmjc4hrQ6WVzWglxAAJ5kgDmvBccyMurPeLXh27Y/wANUN2vbGyWugqbtTxVNe1x0a6CJzw6UE8gWA6nkFOGSXGipVtxbhS83i64es+KLRXXWxGJt1oaauilqKAyAujFRG1xdFuDXFu8DUAkdypeHc1cr8X3irw9hPMfC96utBqaqht93p6ieEA6HfGx5c3Q8jqOR5d6YZG0lzLpRY7qOI/h2o55aWrz+y1gnhe6OWKTFtva+N7To5rgZdQQeRB5hVfEObuU2EaC2XXFmaOELJRXuHrNsqbjfKWmirodGu6SB8jwJWbXsO5pI0e066OGrZY2l1LtRUfEOMsH4RhoajFmLLLZIrnUso6F9yuEVK2qqHjVkURkcA+RwB0a3UnQ6BfbEeJ8NYOs1TiPF+IrZYrTR7esV9zrI6Wmh3ODW75ZCGN1c5rRqeZIHeQowMoqSLxtvFodaRfmXWjdbHU/WxWtnaacwbd3SiQHbs2/Fu10056q3anOHKOjwtTY6rM1cHQYarag0lNepb9SsoJpxu1iZUGTo3P+B/wh2vwO5fCdJwxlF3IqN+euDPpe24f/ADvsn0reaU11uofpGHrFbTAamaGPdukj0572gt+1U+5ZrZXWbFEGCLvmThahxFVFohtNReKeOskLv1QIXPDyTodBpqfBMMZSLpRdXyRxMdJLI1jGAuc5x0DQO8k/JWZh7O7JbF11jsOE84cDXu5ygmOit2I6OpqHgd+kcchcdPuTDYbS4l6oqNaca4Mv94umHrDi+x3K62NzWXSgo7jDNU0Dna7RPExxdETtdoHga6H5FW/VZ8ZGUNCLpXZ1YBpqI1ctB1mbE1EyLrUYaZIN5l06RgewuZruAe3UDcNYwMovlFZNpzxyTv1Ncayx5yYEuNPZ6brlxlpMSUUzKKn1A6WZzJCI2akDc7QcxzVTqcycuaO2Wa9VmYOGYLdiKWOCzVkl3p2wXKWQaxsppC/bO5w/VawuJ8NVOGMplxoraxjmZlxl51b8/wDH+HMNGtcW0wu90gozMR37BK5u7Tx07l9cR5iZfYPstNiTF+PMOWK0Vr2RU1wud1gpaad7mlzWslke1jiWtc4AEkgE9wTDGUXAioj8cYJjvNrw7JjKxMu17gNVbKB1yhFTXQAFxlgiLt8rA0E7mgjQa66Kt/gR94UcCQiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAx9xA3fE1gyUxnfMI/RQuNvtFRU63PpOgbTsYXVBPR/FuEIkLPDeG7vh1UHeGXDuakuc+RFvdRYUdBhzAU2IqLd1gvZaa6YtlfJ4dcPW37dvwd+vJSo46JrrScL+Nq214lns4jpGw1Iihjf16Gd4p3Uri8HY2QzAFzdHjT4TzWFOF/L3GdDxR10FfmlcbgzBGBLHQ1DH26ljbNS1UHSxW8ljBsZE4xvD2/pHbAHOIJCvQxsMxqizURReIzh+w/gLLjPHNrOqGxXfEOLr+ZcH1jJpJKmmgfIehp2hzWhrhH3tbu+FmmugOki8tcZY/wLhXJHLu9Zd3+81GIbG2G83hjXOhsb4aeNzRVu2nRz92z4nDUsPio5XTNvKHMHivxLibiGx/SWqw5V3R9pwnh2qikfFUVUUjmy1kga1wOj4w4A9+rNeTNDM05zZZGtwdb2YrpjU5gwSVOG4xG/W4xMjbI50fLlo17T8WnIhJ8MCnja2kzC35SHT/NKxKQXc7hav/vI1GvjU+maXNHKy/4c1NfhzA1Pfo2tJDnCkcZnN5f2WFSU/KQ7TwlYl2nkbhavw/7ZGrDxph2gxhxY5Q4SuYJpLzljX0M5/sS0NQwn8NdfwUwa2Smsnt7vAo2Dpbpmnifi8rMsJZKyqxVhnD1VZurSbZKhtRQVb2NY7UaOex20akDVw10XXhFykydzJs2W+NsvLlDhLHmWla8Yroo6V3XLiCdrmTBz26RuZuAeGnTc5ugLSBY3BBesb5R2TiOuUVlF3xNge3WmmkoJN7g+SjkuMUkWjRuIa2J4AHP4AB9laseZeBcfcTOSOaGSc0EONMXl1PmBabbG/qrIdjemkka4bdwYZTu8TFGe8amW+OCmP6SvZv5D5P0fHHk/geky+tMFhxPQ3WrvFAyM9FWytpqyRr5ATzIfGw8tOYVucemEKPFWY9BlxhumbTWzLvLSrvUFJTnRsMTJWRNYNdSGgCMD7Ggc/HMGdJ0/KHZAk6fFaLudO7/Uq5Y1zGtuc2ZXE3no7JnC1jvMFLh2mwRdJrnUiJ0FNNTtkkEHxDV/SMd+LBy5hQnwJnuTXiVTiIxVJjfhp4bsVVMgfUXHEeHJKnTU6ziBzZft5Pa/8dVmz8oDz4Tcea+H0cf/AJjTKHNZjSguHBPkjca6rEVNh7MhkVRI4HSOON80znaaa6Br/lry7lIriyz1ymzk4UsyoctMZ0t8fbW2p9WII5G9E19xgDCd7R37XfuUNYwIyTT38kZnsf8AotUP/gNn/wCPUE7naJ8Rfk8srMPUr2snuuZPUInPPwh8r62NpP2AuClTlzxA5PY2yPblzhjG9JX4locBy9Yt7I5A+PoaENl1JaG/CdAefio02Yj/ADIMjgCNDm7R89f/AFqrUwWG89SZPbSSfIuDh1xdU4tzm4d2XGKSO4YewfecO1sMoIfFLRyTRBjtf2g1rAVaOC8rcvM28pc78Q42v2GrHjeqzClobLifENd1YUxjkgmdG2TXvcwzjQAkgjXkARfGD8JVGCPym1VY2l4t9bJcr3Qw9zGitpjJIWg9wMm78QR4LD94vuX9gyEz4ypxt0cWPX5hGvsNtmheajc+anjdLGQNAeibODz12uOgO4a1llPC97j/AAjaBh+Kphyyt8dVdILnPHYYmSVsD98VS8QAGVjv2muIJB8QdVqforDl5buCZmYdup7dDmZQ4wjZbqyGp23FsTdC0NaHBxaOZB05EA6jRbVsF0FXbMn7Hbq6J0NVTYapoJo3D4mPbStDgR8wRooa/k8uHTJnG2W1Hm7ivBcN1xPbb3UQ01RU1U7oWCIRmM9X39CXNJ1BLCddD3gEWoSwmzIqRc3FI9Oc+L6zhf4nsWZiSQuioMwsu6mqpSSGtlvFPHtEIH9bfHCTr9Y+8K8LDw64CouBm3wY1wlR3S9WfCtzxFFVVbXGWG4VUUlQ6UHX9Yaxt5+EbV8vyk+HbXfsN5ZPuUbnPkxhDbi9rtHCGoaGygHw12M/FoUjc8oIqbJHHdNTsbHFFhi5Rsa0cmtFLIAB+CnOcBRalLJD/LfLjA1l/J23vMO04Zo6XEt9wrXw3K5MaemqmMrZNrXknTlsZ8u5WRivMfAeJMlOFbBFgxda7jf8P4msQutup52vqKMgtaekYOY+Igcx3nuWW8GaD8lxKQQR+bVy1P8A77MsUYvwDgSwZG8KeMLFg6x22+3rEti+k7pS0EUVVW66OJmla0Pk1cA74ieYB71VFriy1LOEl0/6ZFuGBMJZuccmb1nzJsVPf6KyYUiNuhqy5zaU9BTEFg1+EgySEEcwXuPfoRgjEMlzxF+T3yvstRWSTujzKmtdIZXE9HG6Ks2M+4GQ6fIH7BpnO95gYTyW44M2r5mZdBY6DEGFIha5543ltUTDTt0ZtB1O6ORv3t071h2a13C18DGTc9wpJKdtwzWZW04mbtL4HNqgx/3O2kjwI5gkEazHCRHFtZ37y78g8a1uMM7eGyC8E/SuGbJe8N1zXEl7XUcdVHGHeIcYRCT9pK2P6ach3eC12WTAjsuvyntJZabcy13StuF/oIy4lreuW+aSbTXu/T9KNB4Bq2Jnv5dyt1Uk8mRQzhphERWi+EREAREQBERAEREAREQBERAEREAREQBERAEREB4L7YLFie2S2XElkt93t8xY6Wkr6ZlRC8scHNJY8FpIc0EajkQD4Lrb8O4etdyrLvbLBbqKvuLY21lXTUkcc1S2NobGJHtAc8NaAG6k6AABVFEGFxLOrsmcnLpW1FyuWUuDKusq5Xz1FRPh+kkkmleS573udGS5ziSSSdSSdVV/zJwWJ7PUjCFk6bD0Zis8gt0IdbmFoaW0526wgtaAQzQaADwVaRTl8yEkin37D1gxXbJbLimw26826ZzXS0dxpY6iF7mnVpLJAWkggEcuRXzdhPCpvFFiB2GbQbnbYDTUdcaKLrFNCQQY45du5jCCRtaQNCVVFy0auAHzUb3wDxxZS7ThbDFiudxvFkw5abdcLrIJK+qo6OOGare0kh0z2AOkILnc3E6bj814rDl7gDDNzrL/AIawNh+03G4a9brKC2QQTVHPU9JIxoc/nz5krB2SvGnh/OXOi75Q0GD6q39QFe6luUlaySOrFLNs1awNBG5urxz5AEc1V6DiysdXxNVvDXUYXnpqymbIIboappjmkZTifYI9NQS3dz1/ZVeJcChThjJmerwthmvvlDiivw5aqi825rmUNxmoo31VM1wc1wjlLd7AQ54IaRqCR4ldrXhrDljq7hcLJh+12+pu8xqLhPSUkcUlXMddXyuaAZHczzdqeZUeKrjZtNNkFfs935f1XVbHiZ2G32/rrA+UtdG0zB+3QD9Jrpp4FevKXjEix7mTb8q8c5Q4owBe71SvrLQLvGdlYxrXPIaS1haC1jyHaFp2ka7tNYUZYIVSOTMjsqcrZbM3D0uWWEn2mOoNW2gNkpjTNnLdplERZtD9vLdprpy1XWlynypoLfW2ihyxwjTUFyEYraWGyUrIakRu3xiVgj2v2u0cNwOh5jmrT4k8/KXh2wHS43q8NT3xtTcorc2mhnETtz2vcHakHl8Gmn2qwMF8Zr8TnGmH7tk3ibDWM8IWKbEAw9dHiGatpomB7gxzmgtcWkaAt0O7kfBSlJrIc4KeGZss+U+VmHaqStw9llhO11MsL6eSWjslLA98L+T2FzIwS09xHcfFepmXmAGWijw9FgXDrbVbqoVtHQttNOKemqQSRNHHt2skBc74wAdSefNYauvGJhqmyRwJnBa8L1N0lx9dYbLQWiKraJI6pz5WStdJoRpG6FzSQOfwhUvNnjSdl9mBiDAWEcoMQ41kwbRR3DEtZQSBkNtjcxr/AInbXahrHAknaO/v2uRKUmQ5wXEkJPhPC1RiKHF0+GLRLfaeHq8N0koonVkcXxfA2bbvDfidyB05n5leSuy+wHdMQ0+MbpgfD9XfqTTq91ntkMlXFp3bJnNL26eGh5LEt94t8KUFjygxHYsP1lzt+bd8pbLSudM2F9BJLLHE4yNIIcWOeQQD+ydDoQris2flHduI2/8AD2MNzxVFitEV2Nz6wDHKHshds6PTUH9MBrr+yo2ZcytuPIys9kcjDG8Ncx42kOGoII7vtGip2H8NYawpQfROFcP2uzUQkMnVbdRx00W92mrtkYA1Og1OnPko/wBFxq4frOJp3DnFg2rDhdJrQb51xnQ9NHTukcOj26/0jTFpr3816s6+LS65WZq0+UuHMmcQY3u9XbG3ONlol3SmMl24CIRucdNhJP2qdiWUiO8jjJna+YXwziiOmjxJhy1XdlFO2pp23CijqRDK3ukYHg7XDno4cwvbW0dFdKOe3XCkhrKSqjdDNBPGJI5WOGjmuadQ5pBIIPI6qOEvG/hWbh4q8/LVg25TMt15bYK+zTTsinpqshhLS/Qjk2SN3d+13BerGPGXh7CnDlhbiCdhGprKfFNU2jgtLKtrZIpNZhIDJoQdhgeDyUOEkO8hxM6QYRwjBh38z6bC9nisDonRG1R0MTaIxuJcW9AG7NpJJI00JJK+U+A8D1VvtdqqsG2CaisUkctqppLZC6KgkZ+o+BhbpEW+BaAR4Lx5XY5izMy4w1mHT259BFiO109ybTPkD3QiVgftLgAHHnzOngroUb+BUkmihYpwJgjHLKdmNsG2LEDaN5kpxdLdDVCFx7yzpGnafuX3vGEsKYioqS3YgwvaLnS2+VlRSQVtDFPHTysBDHxte0hjmgkAgAgE6KrIo3lWF0KZUYVwvV3+mxZVYbtM97o4zFT3OSijdVwsIILWTFu9rdHO5A6fEfmqnrrzRE38xhLgEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAVCx5fxhPA2IcUyfCLTa6ut1/9nE5w/wCrqxvxHYdxbi3IzGeF8CULq2+Xa2PoqSBsjGF7pHNa4BzyGj4C7vI7lMeJTP4WQK4dcO/5NcxeGLGk4lY/G1DeqGdzjzLusShjnHxLm1Efj3AcvBds8GVmGuKPMrOe2NkFTlxiPDN1lLDzdSSlsM8fy0eHMYfsceYWQa7gJuuWdtysx/lVQ3K6Y4sl5s9fiSmmr4m04EbA+q6EO2gDpW6AbnHadOfespScO2LcS5p8REmI7M2DD2Y1hp7dZqzpo3F87YS0P267mlkga4bh+yFf245yYXdyxjHP/hFq5VMFZ+Tkx/WUzt8U+ZkksTh3Oa59MQf3FSDzJ5canD23wOHpj/8GZWNbuFjO+PgVvmTNThFrcXV2L2XOOhFdAQabSn1f0m/Z/3buRIPLu+d+5YZUcSmYnEFg3NvPHCVhwnbMBWqaipIKGpEslY9zHsYNolk0/pC5ziWjRjQAdxLTa34Kknu67j3/lMnlmQtoeGk7cUUTtB3n9HNy/8A5zVKyVpsZZ38W1/z6vGVuIMIYYZhIYfbFe4RHJO9zmcgDoDyDyduoADdSCQsiccOVGOs4MprZhrL+y/Sdxp7/S1ssXTxxaQsZIHO1eQO9w5a6qRLiHHXUn71RtKMS93e1UZrS4d8AYluPE7bsgbzAfzdyZxLe8Tsa9vwiN/QilaAOQ3SdDMPm17/AABWa8l7dY7zxUcVlpxQ5gs9bT2unuBfO6Fgpn00zZSXggsGwu+Id3eNFfWV2U2OcOcYOcGal3s7YMM4poLbBaqsTRu6d8NPTMkbsDtzNHRv7wAduo71i/NbJ/iSwZm7mxiDKLBFqxTYs4rVDQ1UtRWsikt7xAYXENc9nPR73DXVpDm8+Whqck2W4wlDfgonFnZMK5bWDhyoMj7PFd7ZZMdRVNht9PcTUtrqhtRDJHTtne95PSTfBuLjoXfZy9HDvivGeLOO/G2JMxcDPwfepcIx9ZtMk4mMDGNpGscXjkdzAHf+ZV2+cLmZdmwRw0YQtVuprpUZd4vo7ziOaCpY2OmiFVDNMWF+10jW6PaNBqduunPRXTXZRZpUnFhmbmzbMOmWz3nBBttpqetRN6xXtpoGtjALtW/HGRq4ActdeYRSXwkbMs7Xy/BEi2PloXYN4kqmKSN90zyr4y/cCXwzCOTv8QDFO37g5Zu4gMeYpy247rJizB2ALhjS5QYQ2stNDv6aRjumDnjYx50aOZ0HcrTuP5Oi8UfDzR4kstFc5M44+gqJ7e64wikEnWviaNdG6tiIOu/Qubr9ikNFlZmPW8ZOEs46zD/RYfgwcKGvqesROMNY6N+6PZu3O0c/TUcu7mqttFKpzwotdCKM9Laz+T0xRf6S6srK3EWY5ud1p2RuZ9H1b+haaY7hqS1jI36936QaclaeIqipuWSVHkRIHbcvMR44q5GHmRDSW51XC/7umqHN+/VZ0n4X87XcNOZmWzcJNdfL9mXLiG3wCuhLZaBzYWiUOLwB/Ru+EkO005L73vhHzNfm9ntii3YcbJbMT4QudHht3W4Wtqa+tpoBI3YXasPSCZu52g5d+iRms7yJU5NbiTnCh/oz5Yf+Frd/9Fqyssf8PuFr1gbI3AeDsTUnVbvZ7BR0VbBva/opo4gHN3NJB0I7wdCsgLHfFmbBYikERFBUEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAQT7Yvhl8i5nem2/wB6nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/3qdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/AHqdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/ep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv8Aep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv96nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/wB6nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/3qdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/AHqdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/ep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv8Aep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv96nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/wB6nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/3qdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/AHqdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/ep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv8Aep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv96nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/wB6nbF8MvkXM7023+9WnZEBuJ7Yvhl8i5nem2/3qdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/AHqdsXwy+RczvTbf71adkQG4nti+GXyLmd6bb/ep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv8Aep2xfDL5FzO9Nt/vVp2RAbie2L4ZfIuZ3ptv96i07IgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgP/9k=" width="304px" alt="sentiment analysis in nlp"/></p>
<p><p>In this case, is_positive() uses only the positivity of the compound score to make the call. You can choose any combination of VADER scores to tweak the classification to your needs. You don’t even have to create the frequency distribution, as it’s already a property of the collocation finder instance. This property holds a frequency distribution that is built for each collocation rather than for individual words. Another powerful feature of NLTK is its ability to quickly find collocations with simple function calls. Collocations are series of words that frequently appear together in a given text.</p>
</p>
<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width="304px" alt="sentiment analysis in nlp"/></p>
<p><p>For instance, if a customer got a wrong size item and submitted a review, “The product was big,” there’s a high probability that the ML model will assign that text piece a neutral score. In essence, Sentiment analysis equips you with an understanding of how your customers perceive your brand. One of the main reasons behind the success of deep learning in sentiment analysis is its ability to process large amounts of unstructured data with high accuracy. Unlike traditional machine learning techniques that require handcrafted features, deep learning models can learn feature representations directly from raw text data.</p>
</p>
<p><p>This is a typical supervised learning task where given a text string, we have to categorize the text string into predefined categories. Today’s most effective customer support sentiment analysis solutions use the power of AI and ML to improve customer experiences. Regardless of the level or extent of its training, software has a hard time correctly identifying irony and sarcasm in a body of text. This is because often when someone is being sarcastic or ironic it’s conveyed through their tone of voice or facial expression and there is no discernable difference in the words they’re using. Aspect based sentiment analysis (ABSA) narrows the scope of what’s being examined in a body of text to a singular aspect of a product, service or customer experience a business wishes to analyze. For example, a budget travel app might use ABSA to understand how intuitive a new user interface is or to gauge the effectiveness of a customer service chatbot.</p>
</p>
<p><p>And in real life scenarios most of the time only the custom sentence will be changing. You can foun additiona information about <a href="https://deskrush.com/how-artificial-intelligence-is-helping-todays-small-businesses/">ai customer service</a> and artificial intelligence and NLP. Normalization helps group together words with the same meaning but different forms. Without normalization, “ran”, “runs”, and “running” would be treated as different words, even though you may want them to be treated as the same word. In this section, you explore stemming and lemmatization, which are two popular techniques of normalization.</p>
</p>
<p><p>Some popular sentiment analysis tools include TextBlob, VADER, IBM Watson NLU, and Google Cloud Natural Language. These tools simplify the sentiment analysis process for businesses and researchers. In sarcastic text, people express their negative sentiments using positive words. We first need to generate predictions using our trained model on the ‘X_test’ data frame to evaluate our model’s ability to predict sentiment on our test dataset. After this, we will create a classification report and review the results.</p>
</p>
<p><p>Pre-trained transformer models, such as BERT, GPT-3, or XLNet, learn a general representation of language from a large corpus of text, such as Wikipedia or books. Transformer models are the most effective and state-of-the-art models for sentiment analysis, but they also have some limitations. They require a lot of data and computational resources, they may be prone to errors or inconsistencies due to the complexity of the model or the data, and they may be hard to interpret or trust. Sentiment analysis, a transformative force in natural language processing, revolutionizes diverse fields such as business, social media, healthcare, and disaster response. This review delves into the intricate landscape of sentiment analysis, exploring its significance, challenges, and evolving methodologies.</p></p>
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			</item>
		<item>
		<title>What is sentiment analysis? Using NLP and ML to extract meaning</title>
		<link>https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=9443</link>
					<comments>https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=9443#respond</comments>
		
		<dc:creator><![CDATA[prith001]]></dc:creator>
		<pubDate>Mon, 08 Jul 2024 09:28:49 +0000</pubDate>
				<category><![CDATA[Artifical Intelligence]]></category>
		<guid isPermaLink="false">https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=9443</guid>

					<description><![CDATA[Sentiment Analysis with NLP: A Deep Dive into Methods and Tools by Divine Jude By analyzing online conversations, brands gain valuable insights and identify trends. This helps them make data-driven decisions to improve marketing, customer service, and product development. This article will present the top 10 online sentiment monitoring platforms for brands, highlighting their key &#8230;<p class="read-more"> <a class="" href="https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=9443"> <span class="screen-reader-text">What is sentiment analysis? Using NLP and ML to extract meaning</span> Read More &#187;</a></p>]]></description>
										<content:encoded><![CDATA[<p><h1>Sentiment Analysis with NLP: A Deep Dive into Methods and Tools by Divine Jude</h1>
</p>
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" width="307px" alt="what is sentiment analysis in nlp"/></p>
<p><p>By analyzing online conversations, brands gain valuable insights and identify trends. This helps them make data-driven decisions to improve marketing, customer service, and product development. This article will present the top 10 online sentiment monitoring platforms for brands, highlighting their key features, benefits, and applications.</p>
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<p><p>Now, they have billions of words we have only say, a 10k so, training our model with a billion words will be very inefficient. We need to just select out our required word’s embeddings from their pre-trained embeddings. It more like captures the relationships and similarities between words using how they appear close to each other. So, each sample has the same feature set size which is equal to the size of the vocabulary. All the samples of the train and test set are transformed using this vocabulary only. So, there may be some words in the test samples which are not present in the vocabulary, they are ignored.</p>
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<p><p>This model uses convolutional neural network (CNN) absed approach instead of conventional NLP/RNN method. While functioning, sentiment analysis NLP doesn’t need certain parts of the data. In the age of social media, a single viral review can burn down an entire brand. On the other hand,&nbsp;research by Bain &amp; Co.&nbsp;shows that good experiences can grow 4-8% revenue over competition by increasing customer lifecycle 6-14x and improving retention up to 55%.</p>
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<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' src="https://www.metadialog.com/wp-content/uploads/2022/06/examples-of-nlp-1.webp" width="309px" alt="what is sentiment analysis in nlp"/></p>
<p><p>On the Hub, you will find many models fine-tuned for different use cases and ~28 languages. You can foun additiona information about <a href="https://www.rangolitech.com/ai-is-revolutionizing-customer-service-with-human-like-responses/">ai customer service</a> and artificial intelligence and NLP. You can check out the complete list of sentiment analysis models here and filter at the left according to the language of your interest. There are complex implementations of sentiment analysis used in the industry today. Those algorithms can provide you with accurate scores for long pieces of text.</p>
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<p><h2>Guide to Sentiment Analysis using Natural Language Processing</h2>
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<p><p>However, sometimes, they tend to impose a wrong analysis based on given data. For instance, if a customer got a wrong size item and submitted a review, “The product was big,” there’s a high probability that the ML model will assign that text piece a neutral score. In essence, Sentiment analysis equips you with an understanding of how your customers perceive your brand. In conclusion, sentiment analysis is a crucial tool in deciphering the mood and opinions expressed in textual data, providing valuable insights for businesses and individuals alike. By classifying text as positive, negative, or neutral, sentiment analysis aids in understanding customer sentiments, improving brand reputation, and making informed business decisions. Natural Language Processing (NLP) models are a branch of artificial intelligence that enables computers to understand, interpret, and generate human language.</p>
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<p><p>While this difference may seem small, it helps businesses a lot to judge and preserve the amount of resources required for improvement. Agents can use sentiment insights to respond with more empathy and personalize their communication based on the customer’s emotional state. Picture when authors talk about different people, products, or companies (or aspects of them) in an article or review. It’s common that within a piece of text, some subjects will be criticized and some praised. Run an experiment where the target column is airline_sentiment using only the default Transformers. You can exclude all other columns from the dataset except the ‘text’ column.</p>
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<p><p>Ultimately, it gives businesses actionable insights by enabling them to better understand their customers. Sentiment analysis can help you determine the ratio of positive to negative engagements about a specific topic. You can analyze bodies of text, such as comments, tweets, and product reviews, to obtain insights from your audience. In this tutorial, you’ll learn the important features of NLTK for processing text data and the different approaches you can use to perform sentiment analysis on your data. Creating a sentiment analysis ruleset to account for every potential meaning is impossible. But if you feed a machine learning model with a few thousand pre-tagged examples, it can learn to understand what “sick burn” means in the context of video gaming, versus in the context of healthcare.</p>
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<p><p>This is a popular way for organizations to determine and categorize opinions about a product, service or idea. The primary role of machine learning in sentiment analysis is to improve and automate the low-level text analytics functions that sentiment analysis relies on, including Part of Speech tagging. For example, data scientists can train a machine learning model to identify nouns by feeding it a large volume of text documents containing pre-tagged examples. Using supervised and unsupervised machine learning techniques, such as neural networks and deep learning, the model will learn what nouns&nbsp;look like. BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model for natural language processing developed by Google. Social media listening with sentiment analysis allows businesses and organizations to monitor and react to emerging negative sentiments before they cause reputational damage.</p>
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<p><p>But it can pay off for companies that have very specific requirements that aren’t met by existing platforms. In those cases, companies typically brew their own tools starting with open source libraries. All the big cloud players offer sentiment analysis tools, as do the major customer support platforms and marketing vendors. Conversational AI vendors also include sentiment analysis features, Sutherland says.</p>
</p>
<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width="307px" alt="what is sentiment analysis in nlp"/></p>
<p><p>The obvious disadvantage is that this type of system requires significant effort to create all the rules. Plus, these rules don’t take into consideration how <a href="https://play.google.com/store/apps/datasafety?id=pl.edu.pg.chatpg&amp;hl=cs&amp;gl=US">Chat GPT</a> words are used in a sentence (their context). Though new rules can be written to accommodate complexity, this affects the overall complexity of the analysis.</p>
</p>
<p><h2>Brand Monitoring</h2>
</p>
<p><p>You can then implement the application that analyzes sentiment of the text data stored in Elastic. Language is a complex, imperfect, and ever-evolving human communication tool. Because sentiment analysis relies on language interpretation, it is inherently challenging. As automated opinion mining, sentiment analysis can serve multiple business purposes.</p>
</p>
<p><p>Nike, a leading sportswear brand, launched a new line of running shoes with the goal of reaching a younger audience. Sentiment analysis and Semantic analysis are both natural language processing  techniques, but they serve distinct purposes in understanding textual content. Negation is when a negative word is used to convey a reversal of meaning in a sentence. Irony, sarcasm, and contextThe challenge of detecting and understanding in-person irony and sarcasm also extends to sentiment analysis. Sarcasm uses positive words to describe negative feelings, and the issue is that there are often no textual clues for a machine to distinguish earnestness from sarcasm or irony.</p>
</p>
<p><p>The corpus of words represents the collection of text in raw form we collected to train our model[3]. GridSearchCV() is used to fit our estimators on the training data with all possible combinations of the predefined hyperparameters, which we will feed to it and provide us with the best model. Now comes the machine learning model creation part and in this project, I’m going to use Random Forest Classifier, and we will tune the hyperparameters using GridSearchCV. For example, most of us use sarcasm in our sentences, which is just saying the opposite of what is really true. We can see that the input dimension is of size equal to the number of columns for each sample which is equal to the number of words in our vocabulary.</p>
</p>
<p><p>Semantic analysis is a computer science term for understanding the meaning of words in text information. It uses machine learning (ML) and natural language processing (NLP) to make sense of the relationship between words and grammatical correctness in sentences. Aspect-based analysis focuses on particular aspects of a product or service. For example, laptop manufacturers survey customers on their experience with sound, graphics, keyboard, and touchpad. They use sentiment analysis tools to connect customer intent with hardware-related keywords. Marketers might dismiss the discouraging part of the review and be positively biased towards the processor&#8217;s performance.</p>
</p>
<p><p>Watsonx Assistant&nbsp;automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently. The purpose of using tf-idf instead of simply counting the frequency of a token in a document is to reduce the influence of tokens that appear very frequently in a given collection of documents. These tokens are less informative than those appearing in only a small fraction of the corpus. Scaling down the impact of these frequently occurring tokens helps improve text-based machine-learning models’ accuracy. Sentiment Analysis is a sub-field of NLP and together with the help of machine learning techniques, it tries to identify and extract the insights from the data.</p>
</p>
<p><p>Smart assistants such as Google&#8217;s Alexa use voice recognition to understand everyday phrases and inquiries. They then use a subfield of NLP called natural language generation (to be discussed later) to respond to queries. As NLP evolves, smart assistants are now being trained to provide more than just one-way answers. ChatGPT is an advanced NLP model that differs significantly from other models in its capabilities and functionalities.</p>
</p>
<p><p>Gain a deeper understanding of machine learning along with important definitions, applications and concerns within businesses today. Launch your sentiment analysis tool with Elastic, so you can perform your own opinion mining and get the actionable insights you need. And, because of this upgrade, when any company promotes their products on Facebook, they receive more specific reviews which in turn helps them to enhance the customer experience. So, for this part, we need a Recurrent neural network to give a memory to our models. If we think about telling something about someone&#8217;s statements, we will generally listen to the whole statement word by word and then make a comment. It will look at each word in a temporal manner one by one and try to correlate to the context using the embedded feature vector of the word.</p>
</p>
<p><h2>Problems, use-cases, and methods: from simple to advanced</h2>
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<p><p>Once you’re familiar with the basics, get started with easy-to-use sentiment analysis tools that are ready to use right off the bat. We will use the dataset which is available on Kaggle for sentiment analysis using NLP, which consists of a sentence and its respective sentiment as a target variable. This dataset contains 3 separate files named train.txt, test.txt and val.txt. This text extraction can be done using different techniques such as Naive Bayes, Support Vector machines, hidden Markov model, and conditional random fields like this machine learning techniques are used.</p>
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<p><p>A Sentiment Analysis Model is crucial for identifying patterns in user reviews, as initial customer preferences may lead to a skewed perception of positive feedback. By processing a large corpus of user reviews, the model provides substantial evidence, allowing for more accurate conclusions than assumptions from a small sample of data. Over here, the lexicon method, tokenization, and parsing come in the rule-based. The approach is that counts the number of positive and negative words in the given dataset. If the number of positive words is greater than the number of negative words then the sentiment is positive else vice-versa.</p>
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<p><p>These tools utilize NLP and machine learning to analyze your text data, offering insights into public perception and sentiment trends. Popular platforms include SEMrush, Brandwatch, and Alchemer, which provide detailed sentiment insights driven by robust analytical techniques. For deep learning, sentiment analysis can be done with transformer models such as BERT, XLNet, and GPT3. The basic level of sentiment analysis involves either statistics or machine learning based on supervised or semi-supervised learning algorithms. As with the Hedonometer, supervised learning involves humans to score a data set. With semi-supervised learning, there’s a combination of automated learning and periodic checks to make sure the algorithm is getting things right.</p>
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<ul>
<li>One of the most prominent examples of sentiment analysis on the Web today is the Hedonometer, a project of the University of Vermont’s Computational Story Lab.</li>
<li>As a human, you can read the first sentence and determine the person is offering a positive opinion about Air New Zealand.</li>
<li>Sentiment analysis is used for any application where sentimental and emotional meaning has to be extracted from text at scale.</li>
<li>We can use pre-trained word embeddings like word2vec by google and GloveText by Standford.</li>
<li>Researchers also found that long and short forms of user-generated text should be treated differently.</li>
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<p><p>Adding a single feature has marginally improved VADER’s initial accuracy, from 64 percent to 67 percent. More features could help, as long as they truly indicate how positive a review is. You can use classifier.show_most_informative_features() to determine which features are most indicative of a specific property. With your new feature set ready to use, the first prerequisite for training a classifier is to define a function that will extract features from a given piece of data. This time, you also add words from the names corpus to the unwanted list on line 2 since movie reviews are likely to have lots of actor names, which shouldn’t be part of your feature sets. Notice pos_tag() on lines 14 and 18, which tags words by their part of speech.</p>
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<p><p>This helps businesses and other organizations understand opinions and sentiments toward specific topics, events, brands, individuals, or other entities. Similarly, in customer service, opinion mining is used to analyze customer feedback and complaints, identify the root causes of issues, and improve customer satisfaction. Natural language processing (NLP) is one of the cornerstones of artificial intelligence (AI) and machine learning (ML).</p>
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<p><p>Instead, it is assigned a grade on a given scale that allows for a much more nuanced analysis. For example, on a scale of 1-10, 1 could mean very negative, and 10 very positive. Rather than just three possible answers, sentiment analysis now gives us 10. The scale and range is determined by the team carrying out the analysis, depending on the level of variety and insight they need. A. Sentiment analysis is a technique used to determine whether a piece of text (like a review or a tweet) expresses a positive, negative, or neutral sentiment. It helps in understanding people’s opinions and feelings from written language.</p>
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<p><h2>Applications of Sentiment Analysis</h2>
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<p><p>As a human, you can read the first sentence and determine the person is offering a positive opinion about Air New Zealand. The second sentence is offering a negative opinion, and the last is also a negative opinion, although it’s a little harder to parse. Note that you build a list of individual words with the corpus’s .words() method, but you use str.isalpha() to include only the words that are made up of letters. Otherwise, your word list may end up with “words” that are only punctuation marks. NLTK provides a number of functions that you can call with few or no arguments that will help you meaningfully analyze text before you even touch its machine learning capabilities. Many of NLTK’s utilities are helpful in preparing your data for more advanced analysis.</p>
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<p><p>The hybrid approach is useful when certain words hold more weight and is also a great way to tackle domains that have a lot of jargon. Negative comments expressed dissatisfaction with the price,&nbsp;packaging,&nbsp;or fragrance. Graded sentiment analysis (or fine-grained analysis) is when content is not polarized into positive, neutral, or negative.</p>
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<p><p>For example, say we have a machine-learned model that can classify text as positive, negative and neutral. We could combine the model with a rules-based approach that says when the model outputs neutral, but the text contains words like “bad” and “terrible,” those should be re-classified as negative. Hence, after the initial preprocessing phase, we need to transform the text into a meaningful vector (or array) of numbers. Our aim is to study these reviews and try and predict whether a review is positive or negative. It can help to create targeted brand messages and assist a company in understanding consumer’s preferences.</p>
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<p><p>Now comes the machine learning model creation part and in this project, I’m going to use&nbsp;Random Forest Classifier, and we will tune the hyperparameters using GridSearchCV. Keep in mind, the objective of sentiment analysis using NLP isn’t simply to grasp opinion however to utilize that comprehension to accomplish explicit targets. It’s a useful asset, yet like any device, its worth comes from how it’s utilized.</p>
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<p><p>Language serves as a mediator for human communication, and each statement carries a sentiment, which can be positive, negative, or neutral. Yes, sentiment analysis is a subset of AI that analyzes text to determine emotional tone (positive, negative, neutral). By analyzing Play Store reviews’ sentiment, Duolingo identified and addressed customer concerns effectively. This resulted in a significant decrease in negative reviews and an increase in average star ratings.</p>
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<ul>
<li>If the number of positive words is greater than the number of negative words then the sentiment is positive else vice-versa.</li>
<li>Sentiment analysis algorithms analyse the language used to identify the prevailing sentiment and gauge public or individual reactions to products, services, or events.</li>
<li>This will create a frequency distribution object similar to a Python dictionary but with added features.</li>
<li>To understand the potential market and identify areas for improvement, they employed sentiment analysis on social media conversations and online reviews mentioning the products.</li>
<li>Sentiment analysis is often used by researchers in combination with Twitter, Facebook, or YouTube’s API.</li>
</ul>
<p><p>For example, in response to &#8220;Do you like pulp in your orange juice?&#8221;, &#8220;Omg, you bet&#8221; could be understood as either positive if the author were sincere, or negative if the author were being sarcastic. Sentiment analysis vs. natural language processing (NLP)Sentiment analysis is a subcategory of natural language processing, meaning it is just one of the many tasks that NLP performs. Natural language processing gives computers the ability to understand human written or spoken language. NLP tasks include named entity recognition, question answering, text summarization, language identification, and natural language generation.</p>
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<p><p>Rule-based sentiment analysis uses manually-written algorithms — or rules — to evaluate language. These rules use computational linguistics methods like tokenization, lemmatization, stemming and part-of-speech tagging. Fine-grained sentiment analysis, or graded sentiment analysis, allows a business to study customer ratings in reviews. Fine-grained analysis also refines the polarities into very <a href="https://www.metadialog.com/blog/sentiment-analysis-and-nlp/">what is sentiment analysis in nlp</a> positive, positive, neutral, negative, and very negative categories. So, for example, a 1-star review will be considered very negative, a 3-star review—neutral, and a 5-star review will be seen as very positive. Logistic regression is a statistical method used for binary classification, which means it’s designed to predict the probability of a categorical outcome with two possible values.</p>
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<p><p>Until now we have tried to extract some features from all the words in a sample at a time. He/she will not only consider what were the words used, but humans will also consider how they are used, that is, in what context, and what are the preceding and succeeding words? So, until now we have focused on what were the words used only, so, now let’s look at the other part of the story.</p>
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<p><p>Now, we will create a Sentiment Analysis Model, but it’s easier said than done. As the name suggests, it means to identify the view or emotion behind a situation. It basically means to analyze and find the emotion or intent behind a piece of text or speech or any mode of communication.</p>
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<p><h2>Step7: Bag of Words</h2>
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<p><p>For example, if a customer expresses a negative opinion along with a positive opinion in a review, a human assessing the review might label it negative before reaching the positive words. AI-enhanced sentiment classification helps sort and classify text in an objective manner, so this doesn’t happen, and both sentiments are reflected. This process involves creating a sentiment analysis model and training it repeatedly on known data so that it can guess the sentiment in unknown data with high accuracy. A sentiment analysis task is usually modeled as a classification problem, whereby a classifier is fed a text and returns a category, e.g. positive, negative, or neutral. Rules-based sentiment analysis, for example, can be an effective way to build a foundation for PoS tagging and sentiment analysis. This is where machine learning can step in to shoulder the load of complex natural language processing tasks, such as understanding double-meanings.</p>
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<p><p>As we humans communicate with each other in a Natural Language, which is easy for us to interpret but it’s much more complicated and messy if we really look into it. So, there must a maintained array of 64 weights, one corresponding to each x, for each node or unit of the network. LSTM operates on two things a hidden state that is sent from a previous timestamp and a cell state that actually maintains the weight neutralizing the vanishing gradient effect. This model gives an accuracy of 67% probably due to the decreased embedding size. Max pool layer is used to pick out the best-represented features to decrease sparsity.</p>
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<p><p>This will create a frequency distribution object similar to a Python dictionary but with added features. While this will install the NLTK module, you’ll still need to obtain a few additional resources. Some of them are text samples, and others are data models that certain NLTK functions require.</p>
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" width="302px" alt="what is sentiment analysis in nlp"/></p>
<p><p>A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale. The potential applications of sentiment analysis are vast and continue to grow with advancements in AI and machine learning technologies. We will explore the workings of a basic Sentiment Analysis model using NLP later in this article. Sentiment Analysis in NLP, is used to determine the sentiment expressed in a piece of text, such as a review, comment, or social media post. The sentiments happy, sad, angry, upset, jolly, pleasant, and so on come under emotion detection. Fine-grained, or graded, sentiment analysis is a type of sentiment analysis that groups text into different emotions and the level of emotion being expressed.</p>
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<p><p>However, since our model has no concept of sarcasm, let alone today’s weather, it will most likely incorrectly classify it as having  positive polarity. Binary sentiment analysis categorizes <a href="https://chat.openai.com/">https://chat.openai.com/</a> text as either positive or negative. Since there are only two categories in which to classify the content, these systems tend to have higher accuracy at the cost of granularity.</p>
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<p><p>This will cause our vectors to be much longer, but we can be sure that we will not miss any word that is important for prediction of sentiment. Uncover trends just as they emerge, or follow long-term market leanings through analysis of formal market reports and business journals. By using this tool, the Brazilian government was able to uncover the most urgent needs – a safer bus system, for instance – and improve them first.</p>
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<p><p>Sentiment analysis works best with&nbsp;large data sets written in the first person, where the nature of the data invites the author to offer a clear opinion. Sentiment analysis is often used by researchers in combination with Twitter, Facebook, or YouTube’s API. A popular use case is trying to predict elections based on the sentiment of tweets leading up to election day. After you’ve installed scikit-learn, you’ll be able to use its classifiers directly within NLTK.</p>
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<p><p>Once the model has been trained using the labeled data, we can use the model to automatically classify the sentiment of new or unseen text data. By analyzing sentiment, we can gauge how customers feel about our new product and make data-driven decisions based on our findings. This technique provides insight into whether or not consumers are satisfied and can help us determine how they feel about our brand overall.</p>
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<p><p>There are considerable Python libraries available for sentiment analysis, but in this article, we will discuss the top Python sentiment analysis libraries. At the core of sentiment analysis is NLP – natural language processing technology uses algorithms to give computers access to unstructured text data so they can make sense out of it. These neural networks try to learn how different words relate to each other, like synonyms or antonyms.</p>
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<p><p>For example, words in a positive lexicon might include “affordable,” “fast” and “well-made,” while words in a negative lexicon might feature “expensive,” “slow” and “poorly made”. ML sentiment analysis is advantageous because it processes a wide range of text information accurately. As long as the software undergoes training with sufficient examples, ML sentiment analysis can accurately predict the emotional tone of the messages. This means sentiment analysis software trained with marketing data cannot be used for social media monitoring without retraining. Using pre-trained models publicly available on the Hub is a great way to get started right away with sentiment analysis.</p>
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<p><p>Sentiment analysis is the practice of using algorithms to classify various samples of related text into overall positive and negative categories. With NLTK, you can employ these algorithms through powerful built-in machine learning operations to obtain insights from linguistic data. For example, a product review reads, I&#8217;m happy with the sturdy build but not impressed with the color.</p>
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<div style='border: black dashed 1px;padding: 12px;'>
<h3>Sentiment Analysis Techniques in NLP: From Lexicon to Machine Learning (Part 5) &#8211; DataDrivenInvestor</h3>
<p>Sentiment Analysis Techniques in NLP: From Lexicon to Machine Learning (Part .</p>
<p>Posted: Wed, 12 Jun 2024 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMixgFBVV95cUxQNlVXYVdYc0U3bWp2Qy16WE1CeWx6UTdpeldELVNvMk9pUXZDNUNPNUJBOFR0RmdrWkFNdkMtVF8zWDJBVWhTTWJuSWZaWmVvZ09RV1BObDNsenZ6VUJ5aFRtRHpCNERCVzk4RXFSRE9sbTYzYjBkODNKdEw0YWx6eEoyNWJQamJRUjFielhxeE00dkJXaTdJT1I4eGlSdmVqcFJ6bVlzUG1DVkpMSVJTZ244Yi1PSk05QTRGLWJVUFpSandMbmc?oc=5' rel="nofollow">source</a>]</p>
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<p><p>If the net sentiment falls short of expectation, marketers tweak the campaign based on real-time data analytics. In this section, we&#8217;ll go over two approaches on how to fine-tune a model for sentiment analysis with your own data and criteria. The first approach uses the Trainer API from the <img src="https://s.w.org/images/core/emoji/15.0.3/72x72/1f917.png" alt="🤗" class="wp-smiley" style="height: 1em; max-height: 1em;" />Transformers, an open source library with 50K stars and 1K+ contributors and requires a bit more coding and experience.</p>
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<p><p>This property holds a frequency distribution that is built for each collocation rather than for individual words. Since frequency distribution objects are iterable, you can use them within list comprehensions to create subsets of the initial distribution. You can focus these subsets on properties that are useful for your own analysis.</p>
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<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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" width="306px" alt="what is sentiment analysis in nlp"/></p>
<p><p>You can ignore the rest of the words (again, this is very basic sentiment analysis). The simplest implementation of sentiment analysis is using a scored word list. Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form,[77] because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text. Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews.</p></p>
]]></content:encoded>
					
					<wfw:commentRss>https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?feed=rss2&#038;p=9443</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>The best AI chatbots for education</title>
		<link>https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13449</link>
					<comments>https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13449#respond</comments>
		
		<dc:creator><![CDATA[prith001]]></dc:creator>
		<pubDate>Fri, 17 May 2024 12:28:36 +0000</pubDate>
				<category><![CDATA[Artifical Intelligence]]></category>
		<guid isPermaLink="false">https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13449</guid>

					<description><![CDATA[Higher Education Chatbots: Your Ultimate Guide to Enhanced Student and Faculty Services It excels at capturing and retaining contextual information throughout interactions, leading to more coherent and contextually relevant conversations. Unlike some educational chatbots that follow predetermined paths or rely on predefined scripts, ChatGPT is capable of engaging in open-ended dialogue and adapting to various &#8230;<p class="read-more"> <a class="" href="https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13449"> <span class="screen-reader-text">The best AI chatbots for education</span> Read More &#187;</a></p>]]></description>
										<content:encoded><![CDATA[<p><h1>Higher Education Chatbots: Your Ultimate Guide to Enhanced Student and Faculty Services</h1>
</p>
<p><img decoding="async" class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' 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" width="309px" alt="education chatbot examples"/></p>
<p><p>It excels at capturing and retaining contextual information throughout interactions, leading to more coherent and contextually relevant conversations. Unlike some educational chatbots that follow predetermined paths or rely on predefined scripts, ChatGPT is capable of engaging in open-ended dialogue and adapting to various user inputs. As the educational landscape continues to evolve, the rise of AI-powered chatbots emerges as a promising solution to effectively address some of these issues.</p>
</p>
<p><p>Educational chatbots serve as personal tutors for students in this digital age, answering queries and concerns anytime, anywhere. So, whether you&#8217;re confused with an Algebra problem from the last class or have questions about the exam schedules, these AI-based bots are here to aid you. The presence of conversational AI in educational settings enhances the student experience by offering a seamless, interactive, and responsive communication channel. As a tool that supports both current and prospective students, chatbots help educational institutions meet student&#8217;s expectations for fast, efficient support. Education chatbots facilitate various processes by serving as virtual teaching assistants, evaluating papers, retrieving alumni data, updating curriculums, and streamlining admissions. These tools, powered AI, are transforming how educational institutions, from EdTech startups to universities, engage with students and staff.</p>
</p>
<ul>
<li>These guided conversations can help users search for resources in more abstract ways than via a search bar and also provide a more personable and customized experience based on each user’s background and needs.</li>
<li>Beyond gender and form of the bot, the survey revealed many open questions in the growing field of human-robot interaction (HRI).</li>
<li>This automation reduces the administrative burden and improves the accuracy and efficiency of the admissions process, allowing staff to focus on more complex inquiries and personalized student interactions.</li>
<li>Educational institutions rely on having reputations of excellence, which incorporates a combination of both impressive results and good student satisfaction.</li>
<li>For instance, if students consistently receive solutions or information effortlessly through AI assistance, they might not engage deeply in understanding the topic.</li>
</ul>
<p><p>Similarly, Stanford has its own AI Laboratory, where researchers work on cutting-edge AI projects. MIT is also heavily invested in AI with its MIT Intelligence Quest (MIT IQ) and MIT-IBM Watson AI Lab initiatives, exploring the potential of AI in various fields. Chatbots have affordances that can take out-in-the-world learning to the next level.</p>
</p>
<p><p>This paper will help to better understand how educational chatbots can be effectively utilized to enhance education and address the specific needs and challenges of students and educators. At their core, educational chatbots aim to streamline communication within the education sector, making learning experiences more interactive <a href="https://chat.openai.com/">https://chat.openai.com/</a> and responsive. Through real-time dialogue, chatbots answer queries to guide users through complex educational materials and administrative processes. Like all of us, teachers are bound by time and space — but can educational technology offer new ways to make a teacher’s presence and knowledge available to learners?</p>
</p>
<p><p>Current AI trends, such as the natural language processing and machine learning capabilities of tools like ChatGPT, are likely to make chatbots more sophisticated and versatile. This development will bring many benefits to educational institutions, such as early detection of students who need help and personalized tuition. Through these 10 use cases, we saw how chatbots are proving to be a powerful tool in enhancing the student learning experience and making educational institutions operationally more efficient. As technology continues to evolve, the role of chatbots in education will continue to expand, offering even more innovative solutions to the challenges faced by students and educators alike. One of the most prominent educational chatbot examples is student support, and for a good reason. By 2025, the e-learning industry&nbsp;is estimated to be worth $325 billion, indicating the pressing need for round-the-clock student support and assistance.</p>
</p>
<p><h2>What is an educational chatbot?</h2>
</p>
<p><p>Meanwhile, North Korea, China, and Russia, in particular, contended that the U.S. might employ ChatGPT for disseminating misinformation. Italy became the first Western country to ban ChatGPT (Browne, 2023) after the country’s data protection authority called on OpenAI to stop processing Italian residents’ data. They claimed that ChatGPT did not comply with the European General Data Protection Regulation. However, after OpenAI clarified the data privacy issues with Italian data protection authority, ChatGPT returned to Italy. These examples highlight the lack of readiness to embrace recently developed AI tools. There are numerous concerns that must be addressed in order to gain broader acceptance and understanding.</p>
</p>
<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width="300px" alt="education chatbot examples"/></p>
<p><p>Implementing this tactic helps the online school come off as more open and friendly to new visitors — you rarely expect to open an online chat and have someone greet you there in person. Still, if you want to implement this strategy, remember to add captions to your eLearning chatbot video, just like Lingoda did, to account for people with hearing impairments. Here, we will review several education chatbot examples to give you a better idea of how it all works. Design and set up Facebook, Instagram, WhatsApp, or Telegram chatbots without needing to code with SendPulse. Create message flows including not only text, but images, lists, buttons with a link, and much more.</p>
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<p><p>If a student frequently struggles with a particular concept, the chatbot can offer revised explanations, additional resources, or slower-paced guidance. Imagine a student preparing for an exam late at night and needing clarification on a complex topic. Normally, they&#8217;d have to wait until the next day for help, risking a break in study momentum and added stress. These education chatbots provide answers at any hour, supporting students continuously and making learning stress-free.</p>
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<p><p>Repetitive tasks can easily be carried out using chatbots as teachers’ assistants. With artificial intelligence, chatbots can assist teachers in justifying their work without exhausting them too much. This, in turn, allows teachers to devote more time and attention to designing exciting lessons and providing learners with the personalized attention they deserve.</p>
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<p><p>Many brands are successfully using AI chatbots for education in course examinations and assessments. However, these tests require regular syllabus updates to maintain the course’ quality and standards. Also, educators can’t take a class regularly and focus on the faster completion of the courses.</p>
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<p><p>Whether a student needs help with homework late at night or wants to clarify a doubt over the weekend, chatbots are available 24/7 to assist. One of the critical areas where chatbots prove invaluable is in streamlining the admission processes. Handling hundreds of applications with diverse requirements can be daunting and prone to errors when done manually. Chatbots, however, can automate much of this process, from gathering initial student data to answering common questions about courses, fees, and application deadlines. In recent years, chatbots have become a crucial component in the digital strategy of educational institutions.</p>
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<p><h2>IT Support</h2>
</p>
<p><p>Carnegie Mellon University has developed an&nbsp;AI&nbsp;tutor called ALEKS (Assessment and Learning in&nbsp;Knowledge Spaces) that provides personalized learning experiences for students. Since 2001, politicians, school principals and teachers have been telling us that no child should be left behind. The educational problems that couldn&#8217;t be solved by rules, acts and laws, will finally disappear in the next few decades. This is a fact thanks to fast technological advance and beneficial cooperation between socially aware corporations and educational institutions.</p>
</p>
<p><p>By answering prospective students’ queries on courses, admissions, and the application process, chatbots simplify and speed up the enrolment process. If you’ve got interactive content, such as video tutorials, chatbots can tap into your library and provide relevant content to help students study. Not to mention, some chatbots can facilitate learning, e.g. by playing music for concentration by integrating with music apps like Spotify.</p>
</p>
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" width="302px" alt="education chatbot examples"/></p>
<p><p>SPACE10 (IKEA’s research and design lab) published a fascinating survey asking people what characteristics they would like to see in a virtual AI assistant. Beyond gender and form of the bot, the survey revealed many open questions in the growing field of human-robot interaction (HRI). Claude, the name of the large language model and chatbot developed by Anthropic, uses a different method of training from GPT and Bard that aims to focus on safety and helpfulness. Bard, a generative AI chatbot developed by Google, relies on the Pathways Language Model (PaLM) large language model. Roleplay enables users to hone their conversational abilities by engaging with virtual characters.</p>
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<p><h2>Are there any security concerns?</h2>
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<p><p>Many educational chatbots are equipped with NLP processing, which helps them know the students better through interactions. These bots can detect emotions, tones, and sentiments used by the learners in their texts, classifying them as positive, negative, or neutral. This helps an AI chatbot understand how a person feels after a specific module, which, in turn, helps gauge their satisfaction levels. Education Chatbots powered by artificial intelligence (AI) is changing the game by providing personalized, interactive, and instant support to students and educators alike. With their ability to automate tasks, deliver real-time information, and engage learners, they have emerged as powerful allies.</p>
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<p><p>If students end up being confused and unclear about the topic, all the efforts made by the teachers go in vain. When prompting a chatbot, ask it &#8220;What more would you need to make this interaction better?&#8221; (Chen, 2023). This can in turn prompt you to give more specific details and instructions that can yield better results. According to the survey conducted among 1,000 secondary school attendees, 67% of learners admitted using AI tools. What’s more interesting is that 42% of those surveyed apply this technology in math, while 41% use it for writing essays. This learning concept involves repeating the old lessons, just before you forget them.</p>
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<div style='border: black dashed 1px;padding: 12px;'>
<h3>Google’s new LearnLM AI model focuses on education &#8211; The Verge</h3>
<p>Google’s new LearnLM AI model focuses on education.</p>
<p>Posted: Tue, 14 May 2024 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMigwFBVV95cUxNbC1vUGVYSUY4WXVSdEdJV2ViVkNvS0R0dzdSRkhPbWNvZTBoVlVJOHVscjJrMEM3dW9OcVdSZzdMTjk4M0EyY0psNE01TWhORkdqN092M0Zod3dYNkVKSDFLMXJBVVZIVjV0ME1ZaTVVVG5OWWtRTjllSzZVWERzOEpfSQ?oc=5' rel="nofollow">source</a>]</p>
</div>
<p><p>In the cases of CSUN and Georgia State, their chatbots began as an extension of their admissions offices. At CSUN, students were first introduced to CSUNny when they submitted their deposits. The chatbot then guided them through the rest of the enrollment process, reminding them to stay on top of financial aid applications and helping them stay connected until they visited campus for the first time. Chatbots can assist enrolled students with a variety of services, including academic support, campus information, and extracurricular activities, enhancing the overall educational experience.</p>
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<p><p>Learners feel more immersed and invested in their educational journey, driven by the desire to explore new topics and uncover intriguing insights. Metacognitive skills can help students understand how learning works, increase awareness of gaps in their learning, and lead them to develop study techniques (Santascoy, 2021). Stanford has academic skills coaches that support students in developing metacognitive and other skills, but you might also integrate metacognitive activities into your courses with the assistance of an AI chatbot. For example, you and your students could use a chatbot to reflect on their experience working on a group project or to reflect on how to improve study habits. We advise that you practice metacognitive routines first, before using a chatbot, so that you can compare results and use the chatbot most effectively. Keep in mind that the tone or style of coaching provided by chatbots may not suit everyone.</p>
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<p><p>This feedback can help students improve their performance and achieve their educational goals. After all, we all know that these educational chatbots can be the best teaching assistants and give some relief to educators. They can also track project assignments and teachers with individually tailored messages and much more. Educational chatbots are conversational bots with specialized training, which educational institutions and companies specifically use for the client and student interaction. The AI chatbot for education is transforming the way Ed-tech companies and institutions are sharing necessary information and leading conversations. With SendPulse, you can create your own chatbot for Instagram, Facebook, Telegram, and WhatsApp and manage them all from one platform.</p>
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<p><p>This streamlines the student management process and ensures that no potential students slip through the cracks. It&#8217;s designed specifically to enhance student engagement and simplify admissions, helping you provide a seamless experience for prospective students. Educational institutions can also employ education chatbots to manage and streamline library services. They can assist students and faculty by checking book availability, reserving materials, and answering questions about library hours and policies.</p>
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<p><p>Georgia State University developed Pounce, an&nbsp;AI-powered chatbot designed to&nbsp;assist students during the enrollment process. The Summit Learning project and Jill Watson are ideal examples how chatbots can bring constructive change to the learning process and make it more efficient. There are also dozens of simpler bots and Artificial Intelligence apps, used in various schools and colleges. Every chatbot is different, and depends largely on how much content you put in and how robust a conversation you want to design. As a rule of thumb, it takes one person about a month to make a chatbot with 30 different outputs (ie, types of content you want the user to engage with).</p>
</p>
<ul>
<li>When we talk about educational chatbots, this is probably the biggest concern of teachers and trade union organizations.</li>
<li>Education Chatbots powered by artificial intelligence (AI) is changing the game by providing personalized, interactive, and instant support to students and educators alike.</li>
<li>This includes activities such as establishing educational objectives, developing teaching methods and curricula, and conducting assessments (Latif et al., 2023).</li>
<li>They help in obtaining information on fee structures, course details, scholarships, and school events.</li>
<li>Criteria were determined to ensure the studies chosen are relevant to the research question (content, timeline) and maintain a certain level of quality (literature type) and consistency (language, subject area).</li>
<li>This cost-effective approach ensures that educational resources are utilized efficiently, ultimately contributing to more accessible and affordable education for all.</li>
</ul>
<p><p>In the images below you can see two sections of the flowchart of one of my chatbots. In the first one you can see that the chatbot is asking the person how they are feeling, and responding differently according to their answer. The ability to transfer skills and knowledge that you learned to a new situation involves abstract thinking, problem-solving, and self-awareness. Deliberate practice, such as role-playing, can help you develop these transfer skills. AI chatbots can help with developing scenarios, role-playing a situation, and providing feedback.</p>
</p>
<p><p>Not every student learns the same way, and many have learning disabilities requiring one-on-one lessons and extra care. A chatbot tailors learning and lectures by analyzing each student’s needs and subjects or courses that give them the most trouble. ” in cases a blizzard hits or some other cause can be quickly and effectively answered by a helpful bot.</p>
</p>
<p><p>Consider conducting surveys or focus groups to gather input on what services or information students and faculty would like to see provided through the chatbot. Follow this step-to-step guide to enable chatbot Q&amp;A for intended users, e.g., students or instructors. The chatbot for education containing all the information regarding the course proves to be helpful here. Course-related information includes all about fees, the syllabus covered, the date of completion, etc. This will help build transparency and establish a healthy relationship with the parents and students. With a shift towards online education and EdTech platforms, course queries and fee structure is what many people look for.</p>
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<p><p>We encourage you to organize your colleagues to complete these modules together or facilitate a workshop using our Do-it-yourself Workshop Kits on AI in education. Consider how you might adapt, remix, or enhance these resources for your needs. In conversations with other people, we routinely ask for clarifying details, repeat ideas in different ways, allow a conversation to go in unexpected directions, and guide others back to the topic at hand. For example, if you are using a chatbot to reflect on a recent experience and to think of possible next steps, a conversational tone might yield better results.</p>
</p>
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width="302px" alt="education chatbot examples"/></p>
<p><p>In educational establishments where mental support is essential, the absence of sensitive intelligence in chatbots can limit their effectiveness in addressing users’ personal needs. One of the significant advantages of chatbots in education industry is their ability to offer immediate feedback. This quick response mechanism is capable of asking about specific aspects of the session or course. Such programs gather comments on various subjects like study material, teaching approaches, assignments, and more.</p>
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<p><p>While automation is a key component of the structure of AI-enabled bots, the technology can require some human involvement. Until last year, California State University, Northridge&nbsp;used its CSUNny chatbot simply to communicate with first-year students, part of a larger effort to bolster retention. The chatbot should reflect the institution’s values and brand and be designed to communicate in a way that resonates with the target audience. Use Juji API to integrate a chatbot with an learning platform or a learning app.</p>
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<p><p>A higher education chatbot is an AI-powered virtual assistant designed for educational institutions. These chatbots simulate human conversation and provide instant support to students, faculty, and staff. They can answer common questions, provide personalized guidance, and perform administrative tasks.</p>
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<p><p>You can foun additiona information about <a href="https://scienceprog.com/metadialog-addressing-customer-service-challenges-through-ai-powered-support-solutions/">ai customer service</a> and artificial intelligence and NLP. Their interactive and conversational nature enhances student engagement and motivation, making learning more enjoyable and personalized. Overall, students appreciate the capabilities of AI chatbots and find them helpful for their studies and skill development, recognizing that they complement human intelligence rather than replace it. While chatbots serve as valuable educational tools, they cannot replace teachers entirely. Instead, they complement educators by automating administrative tasks, providing instant support, and offering personalized learning experiences. Teachers’ expertise and human touch are indispensable for fostering critical thinking, emotional intelligence, and meaningful connections with students.</p>
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<p><p>57% of people expect the same response times during business and non-business hours. For queries about part-time opportunities, student organizations, etc, a&nbsp;chatbot can guide students to the right resources and offer support for various non-academic matters. Chatbot technology is changing how institutions in the education industry interact with students, streamline processes, and deliver personalized learning experiences.</p>
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<p><p>They help in obtaining information on fee structures, course details, scholarships, and school events. By digitizing enrollment processes and simplifying communication channels, bots reduce the workload for staff. Here chatbots play an important role, as <a href="https://play.google.com/store/apps/datasafety?id=pl.edu.pg.chatpg&amp;hl=cs&amp;gl=US">Chat GPT</a> they can track progress, ensuring continuous interaction through personalized content and suggestions. Since pupils seek dynamic learning opportunities, such tools facilitate student engagement by imitating social media and instant messaging channels.</p>
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<p><p>Overburdened institutional staff can deploy chatbots to help deliver a superior learning experience to their students in a &#8220;hands-off&#8221; way. Any repetitive tasks that are data-driven can be delegated to a bot powered by AI technology. These AI-driven educational assistants can handle student attendance tracking, test scoring, and sending out assignments, reducing a portion of the workload for busy educators. A free chatbot for education can divide the burden for high schools and educational institutions that receive hundreds of admin-related queries daily. Whether your students seek guidance with fee problems, need a quick campus tour, or have questions about enrollment for management courses, these chatbots come in handy. Let’s look at how Georgia State uses higher education chatbots to personalize student communication at scale.</p>
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<p><p>You can start with a free 14-day trial to explore how the University Template can work for your institution. A chatbot can turn a history lesson into an interactive story in which students make decisions that influence the outcome. Active studying makes learning more engaging and helps students understand the material&#8217;s real-world application. Join me as I delve into how chatbots are revolutionizing learning and student support. AI aids researchers in developing systems that can collect student feedback by measuring how much students are able to understand the study material and be attentive during a study session. The way AI technology is booming in every sphere of life, the day when quality education will be more easily accessible is not far.</p>
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<p><p>Students, especially at certain times of the  year such as beginning and end of semesters, have lots of questions about their lesson plans, classes, schedules, and school guidelines. When a teacher has dozens of students to teach, it’s time-consuming to answer these same questions one by one. After Jill was trained and introduced to students in 2016, she could pass for an actual human for the whole semester until her identity was revealed. For example, a chatbot designed for college students may use casual language and humor, while a chatbot designed for faculty may be more formal and business-like.</p>
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<p><p>The key difference is that Google Bard is trained on a dataset that includes text from the internet, while ChatGPT is trained on a dataset that includes text from books and articles. Enhanced communication capabilities can help tailor the content and tone of chatbot responses. Chatbots are revolutionizing the way educational institutions and ed-tech companies recommend courses to students. Leveraging the power of AI and machine learning, these virtual assistants offer personalized and timely guidance that aligns with each student’s unique needs and interests.</p>
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<p><p>To ensure the chatbot is equipped to handle various questions and scenarios, it’s important to develop a cohesive knowledge base. This can include information on policies and procedures, campus resources, and frequently asked questions. Chatbots can facilitate student engagement by offering personalized recommendations for clubs, organizations, and events based on students’ interests and goals.</p>
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<p><h2>Real-life examples of chatbots helping in the learning process</h2>
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<p><p>Stanford d.school’s Leticia Britos Cavagnaro is pioneering efforts to extend interactive resources beyond the classroom. She recently has developed the “d.bot,” which takes a software feature that many of us know through our experiences as customers — the chatbot — and deploys it instead as a tool for teaching and learning. Jenny Robinson, a member of the Stanford Digital Education team, discussed with Britos Cavagnaro what led to her innovation, how it’s working and what she sees as its future. The latest chatbot models have showcased remarkable capabilities in natural language processing and generation.</p>
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<p><p>At ChatBot, we’ve created a University Template designed specifically for educational institutions looking to enhance their engagement with prospective students. This template is a powerful tool for streamlining the admission process and improving the overall candidate experience on your website and social media platforms. To summarize, the journey through educational chatbots has uncovered a field of possibilities. These AI tools amplify engagement, offer personalized content, and ensure uninterrupted support. Yet, the limitations of these bots, such as lack of emotional intelligence, demand further attention. But the success stories of the University of Galway and Georgia State University, reveal the transformative potential of such models.</p>
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" width="306px" alt="education chatbot examples"/></p>
<p><p>Education chatbots help students navigate course materials, access library resources, and even connect them with human tutors if their queries are too complex. The constant availability of chatbots means students can learn at their own pace and on their own schedule, which is crucial in today&#8217;s diverse educational landscapes. Whether it’s during a midnight study session or early in the morning before class, chatbots are there to assist. For institutions, this translates to higher satisfaction and potentially better academic performance, as students feel supported whenever they need it. Today, chatbots in education are essential elements for contemporary digital engagement.</p>
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<p><p>For example, they can be very good at handling routine queries and qualifying leads. Visual cues such as progress bars, checkmarks, or typing indicators can help users understand where they are in the conversation and what to expect next. It is a superfast virtual agent that can accurately reply to customer inquiries. To ensure this, you only need to make sure you train it with your knowledge sources, such as course catalogs and syllabi, policies and procedures. We recommend using respond.io, an AI-powered customer conversation management software. You can start with a free trial and later upgrade to the plan that best suits your business needs.</p>
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<p><p>Likewise, bots can collect inputs from all involved participants after each interaction or event. Subsequently, this method offers valuable insights into improving the learning journey. Advancements in AI, NLP, and machine learning have empowered chatbots with the ability to engage in dialogue with students.</p>
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<p><p>Chatbots for learning are AI-powered digital tools designed specifically for the educational sector. These programs use artificial intelligence and natural language processing to engage with pupils, pedagogs, or administrative staff. Their primary aim is to enhance the teaching moments, streamline tasks, and provide personalized support. Navigating the expansive world of educational chatbots reveals a realm where technology meets academia, fostering student engagement, and offering support. These AI-driven programs, tailored for educational settings, aim to provide enriched learning experiences. It’s incredible, but chatbots have been used in education since the early 1970s.</p>
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<p><p>However, you need to design a valid bot flow and input related questions accordingly. Chatbots today find their applications in more than just customer services and engagement. Rather, they are there in every field, constantly helping all to alleviate the extra stress, and so are AI chatbots for education. Consider adopting chatbots on several platforms to make sure that your potential students always find you and feel connected with you. For example, Microverse’s chatbot for eLearning enables you to contact them on Instagram, which is especially convenient if users happen to see their Instagram ad or recommended post.</p>
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<h3>Could AI transform life in developing countries? &#8211; The Economist</h3>
<p>Could AI transform life in developing countries?.</p>
<p>Posted: Thu, 25 Jan 2024 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMimAFBVV95cUxPbTZOUVlTWElRQTUtak9YZlNHdlN3eG5yVkYwTkpLS2xXSnYydERPY1V6WkRNTU1mSFhqTEN4RnNWejd1SUg0bmVWWHEzNVc0MXFkX2RvUjZSTml6WWMzSUh1X3d1TDdTRy03YlhtX3V6TFo4UzFyNGFWcldhNTZQMEoxSjk3WTRlOXlQdGt4bjhtSnB0V2ZDMw?oc=5' rel="nofollow">source</a>]</p>
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<p><p>These AI-powered assistants are vital in fostering a more engaging and effective educational environment. AI chatbots can be attentive to – and train on – students’ learning habits and areas of difficulty. It has been scientifically proven that not everyone understands and learns in the same way. To cater to the needs of every student in terms of complex topics or subjects, chatbots can customize the learning plan and make sure that students gain maximum knowledge – in the classroom and even outside. These intelligent assistants are capable of answering queries, providing instant feedback, offering study resources, and guiding educatee through academic content. Besides, institutions can integrate bots into knowledge management systems, websites, or standalone applications.</p>
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<p><p>Keep up&nbsp;with the developing industry and launch the first chatbot on&nbsp;your school website&nbsp;now! LL provided a concise overview of the existing literature and formulated the methodology. All three authors collaborated on the selection of the final paper collection and contributed to crafting the conclusion. Click the banner below&nbsp;for exclusive content about software in higher education. The system is also set up to flag certain keywords and notify the appropriate entities, such as a university counselor or the campus police.</p>
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Mn0XWJXdTqwqLWg015bzmlCUXiSwERFsPkIi2/gl0Vbi2ICGUkU8MZnnyktc9oc1rYg4bWlznDaNtg61jYrRcXEKFN1J8EsmylSlUmoR4s08uA3kfSFkKaKwA2XPOTYXPSeYLrKm0dooYTDBSU8TMpaAyGMbxa52Xc70nauWcTo3000tPILPhe6Nw/NNgfURY/OFEaP0vG+lKKjq48858zR7Q2M7WnDflNvPr3I2JmEYfK6qpoJJ5JqWlmqBWCSJ9JM6mZnlAiay7IDta1+c/BvvWnVrRlzdG0n0c62LXOiwtrY9W3wqqkE72yNM0kcTWGOF8Y8ZkIeXu6HEjoXpwe4a6rxOjiaMwEzJZNlwI4nB7i70bAP6wWylUdGFSpJtpc/Lj6Zfd3ETH79alGmsN44cnwXnhcX3mmZ29I+kKoIO4g+ors5uG0/UQ9kz8Fjse0Uw+tidFUUkDwQQHCNrJGX545GjMx3qKio+1ccrWp7vX+xd5aBljdP8jkNT5xY/cNd8sb9hGoc02wJ2G19TROdnEL/Efzvie1skTjb4WRzb+kFTHxY/cNd8sb9hGuz2gqxqWGvHg3Fo5tEwcLvVfFZJUrpHtY50cetePJjDmszG4HlO2NHP8ygbBOC/Gqevp60xUrhDVx1LoxVWJDZhI5oJjtewIU/krE4npHQUrxFUVlNFK4gNifMwSm+60d837FSrK9rUFKNJZ1lh7s7iyXVtTqtObxjgaDwmaJYtjk1M0MpqOlpw+xlnMsrny5MztXCwt2BgAGbbc7QvbG+DMtwH8mUcgfOyobVmSX2sVE4GV97X1YLDlbvtkbc7ypApMWpZm5op4ZG5xHdkjHWkdsbGbHY838k7VfL6Wkq8Iwgtyi8pY7/PvZ8/YqUnKT3uSw35eRDOh2jGN02D4nhb6KFnhQqHsmfVROJdLAyLVNijuLnVjxnOaBnJ25bH24ItDMVweqmmnghfHNTmO0dQwva9rg9mxwDSDYjfzhTBZF9VNL1ZxqRwsTeXu7+u4+YaPhFxabzHgQXh/B1jMeLtxMw0pb4e+sMIqvHyyTOkLATHlLgHkc20cyu+FLQDFcXxB1VDFTxxiCOBolqBndkLyXkMYQ25edl+ZTSi2LTdwqkau7Kjqrd3Hy9GUnBw34bz9SL+EHQOpxXD6IhscGIUUQj1bpA6KVhaxsjBK0bNsYc0kc5BAvcYubRzHanAGYRJRwxPgdEGyOq43OmjjmD2NDGXbGGtO1xcSdWAG+NdsyItNPStWEIw3PVlrRz3P+PU2SsKcpOW/esPzRHHAtoviGEsqYauOHJM9szJIps5DmtyFjmlo5gCCL86zmncOJTPpI6KCGSCKpp6mpMs4iMgp5BIyBjcp+G1r8x3FrRt222tLrRVvJ1azrSSy+hthbRhTVNN4LZssmqzmIiTKTqs7Cc3xc98vz3soCrOCbGfCZJ6ZlNA0VD5qdvhN3QjWmSEXEdrtGUf1V0Mi2WWkato5e7xv3PO8+bmzhcY187uRaYPJO6GM1EbYpy0a2NjxIxr9zsjx5TTvHPtV4qKq4W8vJ1JYWAiIsGQiIgCIiAIiICKuMz72Un6Rj+6Vi59XQXGZ97KT9Ix/dKxc+r0f2Y+D/qf7FO018S/RBERWEiQhRUQyEREMhERAEREAREQFFMfBTwTidkdbijHCN1nwUZJaXtO1slQRtDTvEYtzX6FgOArRNuI1zqidualosj3NIu2WdxJijIO9oyl5HoYDsK6U2AKo+0Gl5U5fZ6Tw/8Ac1+i/cn9E6PjNe9qcO5fueVJSRwsbHExkUbBZkcbQxjQNwa1osAvZcrcOvGOmE0uH6OvYxkTnRTYrlbI6R7SWvbQseCwRggjXODs23KALPdzhjOL1da8yVlVU1chJJfUzyzuuTfYZXGw9AVL8yyJYP04uEuF+W+qb8Vv0BNU34rfoQH6kZgl1+SGmtW0ZadgAOx8hFv6jf8AH6FjdHNJcQw2QS4fXVdFICHZ6SolgJI3ZtU4Zh6CgP19ngZI1zHta9jgWua4BzXA7wWnYR61DPClwSMDJKzCWZXNBfLQtuWuA2l1MPguG32vcfg2OwxDxauNfNPUQYTpO5hM72xU2Lta2K0jtjGYhGwBga42aJmAWJGYWu8dkWuuuzvatrPXpv6dz9TnubWnXjqzX15HE5RSjxgtEm0dUyvgblgrXubM0CzY6qxeSLbtY0Od+cx551Fi9MsruN1RjVj3/k+9FLuaDo1HBn0pZ4sfu+u+SM+2aolClniykeH1ovtNG0gdIE7Ln9o+lcenfgqn0/VG/RnxMPr+jJ9ntl2ktGzaDZaBp/we02JO18c+pqrWLzZzJANweOn0qQJb22AO9B/7rx8bqm/S1ec0a06UteDwy33FvTrwcKiyvMgaLgjry+zqilay/lhzibdOW3+Kk3QTQukwphLJXSTvAEkxs0kfFaLeK262vxuqb9LVXxuqb9LV1XOk7ivHVnLdy4focNpoa0tZa9KG/m9+PTJ8eJ1zv1ldR7htvsG3p9K+WMBAu1oPRYGy9AFwEocxcPPv7Vf8Om+wYpA4sfuGv+WN+wjUe8PDwceq7czKZp9B8HjNv2hSFxY/cNf8sb9hGrnpD/o8PSBWrT/qEvWRI+lk8sVDWS099fHS1D4rC51jYnOZYc5uBsXOvA5pHTUOKmornEtqIpIzUvu8xyyvY7WyON3WdZwL+bPc7LldJY3XspaeaokBMcEb5XgWvlYLm1za9gok4TOCQP1lZhQDXnNJJRbAx53k0x3Mdv8AazsPNl3GG0NcUFCdCvuU92tyx3EhpKlVco1ae/V34/c3bSfAI6iswrEoAHSRVTNZJHZwlpHxS5XOLdj2tkMbg7bYPd0q00o4UqDDp5KapgrRNHY5WxQuD2u2sex+uylrht2kemxBCiLgp03qcMqoqWVzn0UszYpIZL3p3PfkMsWbbGQ43czcfG2X2r24xXvy/wCRQf3yrspaFf2pW9d5jqtxa3bjmnpL/RdWmsPKTTJT0i4U6ShfCJaSvMc7NZFOIomxyM8XM6MSStc8DM3bYXuCLggm4x7hKo6anjq2QVlVTSZLVEETRC1z25msc+Z7fbLXBAvYgtNiCFH/ABi/5LBPk9T+7Rr3x/8A1HpPz4fvci0U9G28qdCbT+/PVe/uy1n13GyV5VU6kc/hjlbvJbvzJQ0a0xoq6hfXxvMcEWfX64BjoDG0PeJACRsaWuuCQQ4LBy8KNEaSeughqaimppWRTujELJI9a4NikMMsrX6tziACQP2G2m8CNNTzYHjENW8RU0kzmzSFwZq2Gnju/O7Y0iwO3oCwksOHwYPjUWFGqrGO8D8Lr6hrYYWhlUzUw08dg97yXvJuALEm/ktPzsyhGvOn97dOKXLDazl89+4+ne1XSjJY3xb88rPBctxMuAaaUlfRS1tI2aZsNxLAGMFQwgBxaWOeGuOU32ON7G1zsWCwzhdwypl1UTKu+qklzOiia0iNhe4XMuw2BNzZoAJJABK1Li2H2nGBzBtKbeksq7/3D6Fp3AfRR1OJinlBMU9DVwyAEtJZJDkcARtBs47VueiaEJXCecU0mvqs7+f5Gv7fVkqWMfeyn9HgkjF+FCLEMMxI0EdZBUU8IfrHahhiY6RrWygtmJIvdtmgkX9N1ieL7jNXPUYi+d1VWuLKNpe6ZjzEM9TtPhErSGbb2ZfyTs3Xy2kegdHg2C4u+mdPI+ena1z53sc5rGyMIYwRsaALm52EnZ0LD8V/+UxX/h0X71WvrUtvsNeVFbtaOG+P+3P5mNat9qpKo9+HnHDvwTgFVUCqqoTwREQyEREAREQBERAEREBFXGZ97KT9Ix/dKxc+roLjM+9lJ+kY/ulYufV6P7MfB/1P9inaa+JfogiIrCRJQlEXpHA520bvSsNpGJzjBZk8Hmi9X07h0H1fgvJYTT4GKdWM1mLyERFk2BEXygKkqiIF9YB07wC4YIMFp32AfUvlqXnnOZ5ZHf8As441r3G00ukwrR6WOB5jqMTmbh8b2kh0cUjHyVT2kbQTDG9gI2gytPMt34KHh2C4Zl3CjhafzmNyu/6gVCvHwhccPweQXyNrp2OPMHyUxcy/zRSLyW9m515yfHWf6l8tYqNKKXJHJQC33g/0Gjr4DUTyPawuLWNZa5y7ySVoa23QvTeXDo3Q6ps0RJcGl2Utcd9nW3L7sZUVVzW4HNpKNxKi1bv737Flp3o7+TagRB+eORmeNxFja5BB9IIWs1tQ2KN8jtzGk+s8wHpJsPnWd0rx6XEZ9fKA0BoYxjfJY0Em1+c7SbqPNNMQu5tO07G+PJY73HyW/MNvzjoWm4cHUfu+Gdx0WiqqlFVfxY3mx8GOgwxt09VUyujia+xEdsznnbYE7mgbPmWP4V9CBg8sOrkMkE4dkLtj2ubbM13TvG30ry4OtPZ8GMjREJoZbOdE4lhzDc5rrbDZWvCHppPjEzHyMbFHEC2KJpuG5triXHe47PoCkZTs/saSX+p/n5EXCnf/AG9yb/0scPpy45yasF+l3Ex06lxzRWldUvMlVhsr8LnkcXF0op2Rvp5HucSXvNPLCC4k3cx551+aC7u/+G5TyDBMXlP8m/FWMZ+fFSQmS3zSx/QognCeuGPDG1WC17SLuihNSw84dT+27PW1rm+pxXKi7A07eG4XiRO4UNXf6vIuPwrx7KSbpVI92V+a/sVjTsV7yL8gszofpDPhdXHWQWLmXa+NxIZLE62eN1twNgb8xa081lhkVoq0o1IuE1lPcyFhNwakuJ0RR8N2FOjDpYqyJ9vGYI2SAbOZ7ZPGHzD1KQcXxinpKZ1XUSCKBjQ4udv22yta0bXPJIAaNpJXGxGwjpFlsunWmNVi0rTKckENhBTNN2R2GXO4/DlI3uPTYWCqlz7MQdSKpNqO/WbeccMJeZN0dNTUHr733f3Jr5asG6Kz6uP86ctWDdFZ9XH+dc4ouvsxac5df7GnbVx5dP7nR3LTg3RWfVx/EVjjXDfQNid4JBUTTEHIJWthiB5i92cuI9DRt6RvXP6LMfZm0Tz97r/Yw9M3DWN3QusWxCWqnlqZ3Z5p5HSSO6XO5gOZoFgBzAAKcuLF7hrvljfsI1Aanzixe4a75YPsI09o4KNi0uCcUNESbuk3yZIWnFDJVYbXU0IDpZ6WaGME2BdIwtFydwuVjcJr8RpaZkNZQz1M8MYZrqJ9PJFU5BlbJaeWN8UjgASHNsCTYkLa151E7I2l8jmsYN7nuDWj1uJsF59Gr93UxlZyWmVP72tnG7BCOBcGdfW4rJiFfCyipnVbqvwfWMkmfeUysi9qJa0X8pxN99htuHCxoPi+K4lLU09HaERRwMMlRTNdJq895A0SGzSXbAdthtA3KYKDH6Gofq4Kylmk2+1xVEUj9m/xWOJVtpRpNTYbDr6nW6kPEbpIozKI3kgNEgabtuTYbP7xeWp6Wu/fxklvS1YrHdu7uLZwTsKHumm92ct5I64WdFMUxVmGNp6Ig01M8TayemaGySiEGMWkOYjUk3Gzxht329MS0YxSTRmnwltE7wqOVofeelEYYyV8wkD9btBDgLWvcO2WsTvehemFHi4mdR60tgcxrzLHq7l4c4ZQTcizTvstiWqWkbiioUpRS1JayTW/PHf1NkbOlU1qibessP0/xEHYDwf4q3BsSwyWnEUtRNDUwya+B0LzCYiYJMjy9jnZNhylvSRz22jeg2PtwzEsOfTU8MVTkmaZZmOlfNC6NzY49S4ta12qaMz7Aem+yekWdt13rZUd8lLhwaxw3+RjZdLdve5NfR5/kh7gg0cxXCosSFRh7yahkOqa2opLudGJ2lv8rYfyrTckbGnebA4fgx0GxnCsQhq5qHWRsjkje2KppTIBIzKHND5WtdY22EjYp3uqgrE9MVpOo2l/qYUvosbt+4zHR1Nam9/d4GD03wl9fhtXSRkMkngc2PNuEmxzA4i9hmABIvvUacDGj2M4XU1LJsPyx1Pg7XzyVEQZG2F8pcWsiLnTOIlNhsFwLkKXTiMAJBmhBaSHAyMBBG8EX2FUZidOTlE8Jd0CWMnb6A5aKN7Vp0J0Usxlh7/LkbatvTnUjUb3oumr6VAUUedZVEVLoCqKiqhkIiIAiIgCIiAirjM+9lJ+kY/ulYufV0Fxmfeyk/SMf3SsXPq9H9mPg/6n+xTtNfEv0QREVhIk+oG5nAesn5lsGBCl1jn1dzFHDJIyFpc01EzQBFBrGAmNribl3Q09K12OTI5rjuvY+gHZf1A2WT389r8/Rdclda2Y5a9OJE6QlKNWMsZXnwyZ/HqWnNFSVsVP4I+eaohMLZJZI5Y4Qwipi1zi9oDnOjO0gluyys9FdDavFpJWUZgDomsfIJpHM2PLgC3Kx1/JN/mX3pjV66skIfFJGxsUUOoLjAyGOJgbHBmAIYDm/rZjzqS+LzQPibVVjo5MkuSGMhuxwjLnPcOkXcB/VKh7i6na2fvE/vd2d/F5xv3vCOzRdFXGkNRL7uMSxuzhYzu3LLNS5FcZ+NRfWJP4KciuM/GovrEn8FdEeF/7uT9X/unhf+7k/V/7qv8AaS85roXnYtv59TnfkWxn41D9Yk/gpyK4z8ai+sSfwV0R4WOrk/V/7r3ifmF7EX5iLH6E7S3nNdBsa38+pyBpZotXYXI2OthMee+rkaQ+KS2/JI3ZcX8k2PoWFXVHDTh8c+CV2doJgj8IjNtrJISHAg81xmb6nFcrq2aF0lK8pOU1vTw8d/mQOkbNW9TEeDWTovi54y2bC3UhPtlFM9tucwzudNG71ZnSt/qLL8OWhHsgwSroGFranxaiie6wa2qgOaMFx8lrxniJ5myuKgHg40pfhFcypALoXDVVMY3vhcQSWjdnaQHD1Ec5XVuF10NTDHPBI2SKVofG9puHNO4+g81jtFlUNPWEre4c1+GW9eveiwaKulVpKL4rd/DPzJrqSWCWSCeN8M0Mj4popBlkikY4tex7eZwIIXiu8OGfgQw3SImpDjQYkG5W1sLA9swAsxtZASBO0bAHAteAAM1ti5q054vekGFQVNW7wKqo6WGSolmp6jK9sMLDI97oZ2sdcNBOVpcdmy6gyUIgq8TdSRvlbI9haNmR7mFztzW3ab7/APFaPLpLiLiS6urDckkeFT227d2fcslpLLLVVEdHTsklfmDWxRMdJJLO8hrY42MBc9+0AAX2uK3+l4runEjGyDBcoe0ODZMQwyN4BFwHsdUhzHW+CQCOdAQ5UTvkcXSPc9x3ue4ucfWXG5Xmpr/8K+nPmZn/AOSwvvSzujHE/wBLal7fC20GGx38d09W2d4bzlkdGJA4+gub60BAmCYVUVtRBSUkMlRU1MrIYIYxmfJK8gNY0dNz8y/U7gA0Bbo1o/QYUS108bDNWSNN2yVk51lQWusM0bSRG022tiZzrWuAPi/YRomPCGF1fijm5X4hURtaYg5oa9lHALimjdtubuecxBdbYparKhkLHyyPayONjnve8hrWMaLuc4nYAAgNF4fMabS4PNED7ZWubTMHPld48xt0atrh63t6VzMtv4V9LzjFcZGXFLADFStIsS0kF8zgdznkDZzNawbwVqC9K0FYu1tkpfilvflyXQpmk7lVq2Y8FuQREUyR4REQFQqqgVVgBERAFPnFi9w13ywfYRqA1PnFi9w13ywfYRqA9pfgn6olNDfEr0ZKOL18dLBNUSm0UET5ZDz5I2lzrdJsNy5tp8RqdJsapYat7tRLOS2na4iOCnjDpXsYB/tDHGQZN5JvuAAnPhajc/BcSazeKZzjb4rC17/+lrlBfATIBjtJf4TKlrfzvB5D/c0qv6EpRja17hfiSaXlu4rzJTSdRyr06T/C2s+e86LfglIacUxp4hAwAMiawNazL5Jjy2MbxsIc2xBAIN1GWmtFLT6MYnFNJJK9uJO9tlJdJIz8oxap73Hyjq8m30KX1H3DdVMlwGvLDcRz08TthFnsrIA4bRtsojRtSTuIJ984/qd15CPupNeF/oaPwD49DQUmJySMnltJC8spoXzyBjI5Mz3BgtGwfGcQFJ+hWntBi7pGUrpGyxND3RTMyPyXDc7bEtc25ANjcXHSFHPF+H/l+N/mt+7zLX+LmT+WP+Rn/fpz/gP2Kc0hZU687qo860GmuXDl9CNtLmdONGC4Sz+pND9OaI1M9JTl9ZU07Hulhp9VmGr8trDNIwTPbztZmI3GxXnohwgYfiusZSukM0cZkMEkeSVzBYF0YvlftIGx2zML2utBwbBcKoMdL6WrmxCuMlQYaGCMZKd8rXiU1NXfII42veCPKGzY42B03gCJGN0gG4xVI+bwd5/vAXLHRVCdGpOLl92CksrGXvysct25m77fVjUhGWN8mnjfyxv57zJ6W6X4k7HTTeE1TKZuI0zW0rS2M5NZCTE7V2L73OxziDddB0cxkbmLJI9pGWQNDtnPZpIsfWuaNMP9apP0tS/a066dC+NN04Qp0NVJZgnu+h96MlKU6uX/ALv5IA4zFNG2uonBjQ59NJncGgF2WXxcx+FbMd/SvSooIDoNHIY49YycPY7KM2sdiRicb7yTE5w9Q9CrxmvdlB8ml+2CvME0akrdFqaRtRI7wYVdSyjkDPBJXxT1DsrxGGSkkB1iZCAXXtutJwmoWFtKT4TX5OW44Zxcrqsks/d/aJc8W3EayRtZDI+SSjhbFqjIS4RTOLs0UTnfBLLEtGweLuzbd6rNPaFla3DojLV1hc5hhpmNdkc1pc4Plkc2JpaGuJGbZbao94OuE1roJaF1JT0skdJUy0jqRmrgfJDC+bIYSSWSEMc7Nc5rHcd+u8XhufGszjmc2jqH3O0l5fC0uv0kPdt9K5bvR3vKletVjq6qyorvzweeW7uN9veakKVKnLOXht93l+ZM+jWnWH188lLG98VXE57X0tQzVTZoyRIG7SyQtLTcNcSLX3KIeGnTDEmV89NDU1VLT6iI6gFkTwXR3cS+Lx7H85YjTad1LpRPLD4r48Rglbl2XLtS942b82ZwPTmK9OMGP/Oqj5PT/ZrrsNGUqVxCWMqdNyw9+Hu/ncabq9qToyWcOM8ZW7K3/wAHRuE1BkjBMckdg0WlDQT4o2jK43CvV5U3kN/Nb/cF6qmT4lihwQREXyfYREQBERARVxmfeyk/SMf3SsXPq6C4zPvZSfpGP7pWLn1ej+zHwf8AU/2Kdpr4l+iCIisJEnykb5GbGEFvM14Jt6GkbQPRtVUXxOClxEoRksSWSbtBOCeKogpKyrqC+OeCGo1Ebcv8rG2TI552kDNbZZS5SUmoY2KARRxRtDWMAsGgDds2LA8EuKxVWD0BjcCYKeKmlbfayWBgjc1w5r5Q4dIe0862d9MxxJLbk89z6uYry2/uK1SrKNVt4bWH3F20fZ0KFNOjFLKXDv8AqfF5fjR/tS8vxo/2qvgcfxf+p34p4HH8X/qd+K4juKe2/Gj+gr3iBt41r89ty+I6ZjTdosfWf8SvVDJq/Ct7y4p8jm/dXJhXUXDbi0VNg1Wx7hnqWeDQsv4z3SEBxA6GszOP5vpXLqvXspFqjN9zl+xV9OSTqxXl+4W38Hen9Xg78rPb6R7s0lK9xAvzvhft1UnzEHnHONQRWO4t6deDp1FlMiKVWVKWtB4Z1Xorwj4TiAaGVLIJja9PUlsMgPxW5jll/qEq+4R8IkxPBsUoKZ8bZq7D6qlhkkJ1TZKiB8bHvLASWAuBNgdgXIpI5/2rN4LhuKyNvRw15jtfNA2obFYc5kbZlvnVSuvZmnHfGrqr/u/nKJ2hpqb3Shl+RNHAVwE4PoqwTRt8OxV7bT4pUNGtJd5bKWO5FJCSXbGkudfxnOsLSwFzRgui2OyxNmEOIVZkPkDEfBY4WAX9sdI/O+Z2w5W2DBvJcS1mZZodUtpqqavkxfDJWMZ4LE3EGVsc0jnFpF2gG93R+1lzXEBxBNjaBq2NKm/+amuGUs/lnOPoSkLqpL/Z1f8AbBP11RzwN65YOjukBjnnj8Omgp5HMdIypeHOaGh4lbAZtYW5HMcQAbZrbbEjCY3SYnE3/TY69jNm2pFRq9u6zpPFN/WpChoKnW/BXi/16ZOWrpSdP8VJr/PQ6a0o4QMJw9rtdVxvlH+wgImmJ5gWsNo79Ly0elQLwkcI1Vi5MIBpqIG4p2uu6QjyX1Dx5Z5w0eKPSQCtIHoRWXR+gaFrJTf3pc3wXov/AKQ91pSrWWrwXkERFOEYb5wc8GdVi8fhDpRS0mYtbK5hkfM5ps7VR5mjIDcZibX3XsbbvyDw+cpfqzP4ikfg9hazCcMa0WHgFIfndAxzj6yST86zDtZc2DLc1yb2XnF37QXkqstSWFl4SS/gt9voqh7tayy8EQcg8PnKX6sz+Kqcg8XnKX6sz+Kpf9t6I/pcntvRH9Llzbdvvmfkv4N2y7bwfr/JEI4B4vOUv1Zn8RUdwERecpfqrP4ql+8vQz6StB4aNL5cPp4qandkqqoOLpG74Ym2DnM6HucQ0Hms47wFvttK6QuKipwqb35L+DlvLWztaUqs47l69OJDPCHoVJhErWeEw1TH7jGQyZhte0tPmc5gtucCR6tgWqEW3rasOwtksMlZVVPg8OvEOsMT6iaeoLNa4NYHAkBhDi9zvhAbSVa6RYQaaYwueyVpZHNDNGCGywzMD4pGh21t2kXadxB37zdrW4axSnLWkuLxjOOPluyUerfPPvNTEH55azwyuPca+p84sXuGu+WD7CNQI5tiQd4NlPXFi9w13ywfYRri9pN9i/VFg0K83Ca5MlmaJr2ua4BzXAtc0i4c0ixBHOCCVBWMcGmI4ViEVfhLBVwwztmjhztZPG0HxoHZyBIwtLmZgb2du5zPKpZUOzv6ls5anBrDT4MtFzaQr41uK3prijAUmPyTRtLKCtZOQLw1EXg7WEj4c7/ELAd5ZnNtzTuWlaUYFiTsDqqDUOq6yqxCWcuhMbIQ11WyqL/bZAWstdoG0+L86lTKll8Ubp0pKUVwafTh9DNS395HEn3NdSHuCjAsRw2jxWGpoJxJURtdAGvp3CRwjkj1YIls113tO2wtfbsWE4KdDcWw+v1tRRTRRy0s9NrmSU7zC+XJkkc1st8oLea5U+WSy7ZaZqv3m5f6mM/TlvOZaNgtTe/u8Dn/AIONE8dwrE84w9sg1clO+WSdjYMj3NJmbI27ztY02y3IuLC9xXgx0MxXDMZhlmoZnww66MzRvgyEPifEyQF0guy5Bt5VjuvsU/2VMq2VNO1p6+Yx+9HVfHhv8/M+YaKpx1cN7nlfl/BAWn+h+Jsx51dBRS1VO+rp6phgyuJ1Zic+N4vdjrscLkWsQb77TlhE8ssTXzQGnkdcmEyNkcxt/FD3M8XWWtcNJAOy5tdXdlVcV3fzuIQhNL7qwn5HTb2kaMpST/E8kMcN2jmJYpWQOo6GaSOnhfG6Qvp2Ne50mbxA+UOLbAbSBvWW0ajxOj0fOHjDKk1ojqYWDPS6kGd8rmymTX+S0SXI33bb0qUbJZbHpOboQoOK1YvK4+fHf5mv7ClUlUTeWsf50IX4K+CmopqgVmI6tuSORkVMxwkcTLG6JzpXt8UAMe8WaTfMDcW2+GhmhtfgONMmMElTQObND4TTjWObFILxukhb7YHhzGA2BG8i6m8BLLbPTVeo5ueGpLVa7sd2OWMnxHRlKCio7tV5TIOwnQSuxLHJcSq4HUtH4Z4S1s1hNM2JwMEYiBLmAhjM2a2y9rry4dNCsRqcRNXS0z6mKaCNjtVZz45Iw5hDmXvlIykOGzeNlts7WVMqzDTdeFWNTC+7HVS7sfrnzEtGU3TcMve8t+ZjNG6uomha+opXUjjYNikkZJKGhrdsgju1hLs1mhztgBNiS0ZVUAVVESeXlIkIrCwERFg+giIgCIiAirjM+9lJ+kY/ulYufV0Fxmfeyk/SMf3SsXPq9H9mPg/6n+xTtNfEv0QREVhIkoiIh9GT0e0grcPkMlFUSQOcLOy5Sx4G7PG8Fj7bbEg2uVsh4Wce/nrfq1N/DWkL5K5aljb1Za04Jvm0boXNWCxGTSN55Wsf/nrfqtN/DTlax7+eN+rU38NaMi+Nl2nyo9EbPttfxvqStoTwyVsdSBijxUUr7Nc9kTGSQHmkaImjWM6W2JttG0WOQ4V+EbEaWvbHh1YwUklJBPGWxQStdrc93te9hJBAHOoZVSd3o2D0C5Nh0C5J+crkeg7b3yqqKxjDjjc+T8mbdpVvd6jb48e/0Mhj+OVdfLrqyokqJALNLyLMbztYxoDWD80C6xyIpeEIwWrFYXJHDKTk8t5YWy6AaG1OMTmOG0cEdjPUvBLIwdzWj/aSkbm/OSFrsMbnuaxgzPe5rGNG9z3ENa0eskBdZaFYBHhlDDSRgXY0GV/PLO4AyyH1nd0ANHMoXTmlHZ00ofilw8l3v+CR0bZK4nmXBcfPyMfolwfYZhwbqqds04teoqA2WUnpbcZYv6gHzrI6UTukfDQRHxpyNadhtF43ikc4OrkcQdjmwPYfLCzjCGgvcQ1rQSSdgAG1xJ5gAtW0BmfVz1ldI0AmQRxWv4sb2RyZXA7M4jFMDbZdrjszEDzyrWnVlrTbb8y3U6cYLEVg2ykpmxMbGwWa0WHOfSSfhOJuSTtJJK9HxtIIIBBBBBFwQd4IO8L6Raz7MZU4NE4SgOkjbPcztjeWtluxsbgQb5CWNALmZXbBtvtWFwuF1HWGja8yU0sWsjjdYmEnP4oG4R+1OGy17t2XDnP2asmaxrnPcGta0ue5xsGsaLucT0ABa5o0x0889fI0tz3gga4Wc2FhGcHo8dguDuc2W2whAYrTDgzwzEGuc2FtJUG5E9M1rLu/3sQ8SUdO4+kLn7TLRmqwqpNNUtG0ZopWX1U8fx4ydvoLTtB+Ynrdalws6NtxLDJmBoNRA11RTOA8bWRguMYPRI0FtuktPMFP6I0zUt6ihUeYPdv7vNfwROkNHQqwcoLEl+fkctIgKL0QqR19oJ71Yb+j6P7vGr6TV3N3vBubgXt82xafwL6U09bhtNTh7RVUcMdPLCSA/LE0RxytHwmOa0bRuNxzLe15Bd0pU60oyWHll+t6kZ04uL7kWV4usk+k/gl4usk+k/gr1VsufJuLSKNjtgfJ9Nv7woT4w1M9tdSyG5Y6l1bXH40cji4X6fHap3ste060WhxWmMEhyvac8MgHjRvta/paQbEKQ0Zdq2uI1Gt3f6Mi9MWTvLWVKPHivVHOOitKyerhilZLLDd8kkUIc57xHG5+UNYCQHFrWlw3Ak8ytsWxGSrmdPLlDn5QGsGWOONrQ2OKNvwY2tAAHo6bq70vwqowWoEU0oEhDix1NIS/IQQS5sZzxBwJG3ft3rWZq1zhlY0tHO9wsR+a3p9a9BozhVn76Dymkl+eeu7oeaysrhL3DjjD3/t6439SkzrveRzG30WB/aCp64sXuGu+WD7CNQE0WACn3ixe4a75YPsI1ye0axYNea/UtmhI6teMeSa/IlxEReblzCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiICKuMz72Un6Rj+6Vi59XQXGZ97KT9Ix/dKxc+r0f2Y+D/qf7FO018S/RBERWEiSiLcxwWY95vd9YpP4yryWY95vd9YpP4y4dpWvzI9UdX2St4H0NKKot15LMe83u+sUn8ZOSzHvN7vrFJ/GWVpK1+bHqh9kreB9DSkW68leP+b3fWKT+MnJZj3m8/WKT+Ms7Stfmx/8AZD7JW8D6GlIt05K8e83u+sUn8ZYPSLRjEMOLfDaSWAONmvdldG478oljJZmsDsvfYvunfW9SWrCpFvkmj5lbVYrLi8ehh0RF1Gk2bgrphNjWGsIuPCRJb/gMfOP2xhdUgXXLvA68Nx3Dj/vZW/r0s7B+1wXU9O3n+hUL2pb+0xX/AGr9WWjQa/0n6/sjQuMHpr+QMDmq2wNqHyyw0bInSmEO1+bW+2NY8tIhZKQbHaAvri/1BlwcSPY6OWWqqJ5YnlpMPhRbVQQAtFskdPPTxtHM2No5loXHrY46O0hbuGLQ5vnoq8Nv85H0rfcKoainho8Rw20kdTQUZngyl7JQ2nYY5A1njNJaTZ8dyC9xLZA4ausk2SEqE2WnP0uqrZW4e182y7G1Mzmi+21m0hnuOe8QtY+tRvNp5Fj2MTaOyTOkLY5ddTU94qSbJlE1HJUMe6SUsaZNY55Y0NDmiJ0gGUCR6rFGYpOaalkzQROY+eVly11/GYWPtlLBYFpBOZ9nC7Y3ZtmgiaxrWMAaxjQ1rRua1osAPQArHRzB4aGAQwjZcue43LpHne5xcSdwAAJNg0C5tdYnTzTalwgQskjqKqqqnZKWipI9bUTOsSSG/BYMriT0NeQCGOLQNoQKPdDOFGCurDh9VQ1eF1uzLFVBpY/NfI1sosQ52U5SWhjiMrXOdsUhBAce6S0whrayFos2KrqY2gbg1kz2tH0ALHrL6bPDsTxEjca+s+8SLEL122bdKLfJfoef1V99+rPqKRzHBzHFrm7WuaS1zT0hw2gqS+BzTEUUlfUYhVzyRMpW6uOSaSZz5TKMrIWSON5D07LC5JABKjJFpvbKndU3CXfjf38TZb3EqM1KPcbZpnp/iGJVBm181LE24hp6eaSNsbL/AAywjWyHZdx+awWE/Ltd/Pq363Uf51jkX3Ts6FOKhGCwvI+Z16knrNvJkPy7Xfz6t+t1H+dDjtd/Pq363Uf51j0X39npeFdD597Pmyr3Ekkkkk3JJJJPSSdpKAqiLcljcfB9KfOLF7hrvlg+wjUBqfOLF7hrvlg+wjVf9pfgn6olNDfEr0ZLiKiqvNsFxCKl1VAERUQFUVLogKoqIgKoiIAiIgCIiAIiIAiIgCIiAIiICKuMz72Un6Rj+6Vi59XQXGZ97KT9Ix/dKxc+r0f2Y+D/AKn+xTtNfEv0QREVhIk7RNUPiv8A1U8KHxX/AKqES9LPoKe29LPoK8dPRR4UPiv/AFU8KHxX/qrwrKp0Mb5ZXxRxxtLnvfcNa0C5JJ5rKKse4aMr3Mo6USsBsJp3OjD/AEtiAzZT/SIPoXVbWVa4eKUcnDe6St7RJ1pYzwXf0Jc8KHxX/qp4UPiv/VUV6M8Mcc0jY62BtKHGwnY4yRNPNrAQHMb6RmA57DapSjdI4BzXRkEAgi5BB2ggjeFi5s6tu8VI4Pqzv6F3HWoyzz5r6HpHOHEDK4X6W2CstJsIhrqWalnaHRzRuab72m12vb0Pa6zgeYgLIt3bd6o/cueMnFpridUkmmmcTuBBIO8Eg+sbCqL7n8p35zv7yvhewQeUjz+XEyeideKWvo6gmzYaqB7z0MEjdZ/0Zl2NHuC4lIXWvBfjfh+E0U5cHSCIQzdOuh9reSOa5bm9Twqf7V0H9yr6xf6r9yf0FV3yh9f5LDhw0Odj2BV2HRloqHsbNSFxyt8Kp3iaBrnfAY9zdWXcwkcoV4B+HaKhoY8DxaixF9ZhgfSxmipXVr3w07zEIZYoXF7JYrCLMA5pDGm4JUq8ZDT/ANj+BzzRPDa6rvR0HxmTSMcX1HqhjD5LnZmawHylrPFx4IaGlwOGXFsPpauuxAitlFdTRVL6eN7f9Ggbr2EscI7PcN+eaQbbBU4sZqmm3CPpPpPK7B9HMMqcOgkGWqraiSOOpjjd8Gplhe9uFAtN8l3TuBGUDcZO4EeCii0ZpS2Mior5mNFVWuYGlwFiIIGf7GmBF8u9xALibANkWnp4oI2xQxsijaLMjiY2NjG9DWMADR6kQBQdwtYpJhWk1HXvhM0c9EYIGl7Y9eBn8Jp6aSUiLw6IiKVsb3NzsqajKfFfacVZY5hFJXQPpa2mp6umktrIKmGOeF9jduaOQFpIO0HmQEAY3WTYrjOGuip3U8ksdH4FG90b6sxGpFRLWzGB7o4mQBjiWFxymJ4vne1h6Iq52xMkldsZGx8jj0NY0uJ+gLnDR6Z+j/CN+S2uc3DcTotVQ0wcRBSQyRPqomRRE5Y2CrpapgDRvqCeclS9w2YuKTBqqxtJVAUkY5zrriW3pEQlP0Ldb0XWqRprvaRrrVNSDk+5HM1TOZXvld5Ur3yO9b3Fx/aV5ogXriSisIoEn3sIvosI5j9BXyiaZhNPgERFnJkIiJkBERMgqFPvFi9w13ywfYRqAgp94sXuGu+WN+wjVf8AaX4J+qJTQ/xK9GShjFXqIXy5c2QA5b2vcgb7eledLWufHDJlaBKA513nxGlhffydu63MvrHKZ01PLG22Zzdl9guCHAX5tytKGGTwaOJ8RDo4i1zXFtnERuZlBDjsN968rnOoqzS/Dq/nnf8AkXdKHu89+fywfeJYm0Ql8MkRdsLXSkiMjMGk3G8c3rXuMUhaLPlYHNY17wDewIHjdOXaNvpC12TCKpsVTAxrnQnJqA5zM1y9r32JdYN2G995AK9J8MqHPncITZ9IyFvjR7XgR3HlbPJO30Lid3cZzqd30z97OOi6o3+4peL/AOfd/l9DYZMTgaQDKwXDXb+Z/kkkbADzXVviuKtip/CGDWNuBvy3u7L0dK1uqpSxu2B0jY6eFtUGzMaWviaHWdsOzKGnxelZrHY31NCBDG7NIInBhLWlouHWdmI3Bff2ytOE8LDUcrGc5+u58vUx7mmpRy9ze/0PSPHmNlEcoEd4myh5ddlnAbHGwsdtl74jjMMMZfcv2MdZliS15s1wvsy+n0FWs1HTx07nztEZdE2OR7/GcNjWgAgmwuBsCsDh078OMVg+UODWgEC8cUpttJsd7reiySrXEE47m9Vtc1yTRiNOlJp8FlJ/u0ZAY1lqXxyZGQthbKHkEOGbJsdt3bTzLISYlAy2aRguwPG2/iGwDzbc3aNp2bVgKzD6h8lQ8QutJSNibd0d84yXB8bZuO1eTcKqGh/tZdrKJsAGZniSAMaWnba3ik33LXG7uI5Wrne/pxwfTo0Xh62OH7ZNjlxOBpIdKwENzkXucmzxtnNtG1XUMgeA5pBa4AgjaCDuI9C1P8kVDS4ZMw8A8HDg5ljLYbNrr25rnoWw4FC6OniY8ZXsYGuBINiPSDYrrtrirUm4zjhYNVWlCMcxlkvkRF3HOEREAREQBEXy94AudgG0k7gBzlDB9ItF0h4WcConFj61s8jb3jpGPqdo2EGSMaoH0FwWsVHGCwppsyjxF/pyUzR+2e66YWdae9RZzzu6MXhyRMKKG4eMJhhNnUOINHSBTO//ALhZ/BeGbAakhpqX0rjsAq4XxN+eVuaNo/OcFmVlWjvcWYjeUZblJGP4zPvZSfpGP7pWLn1T7xj6iOXCaKSN7JI34hE5j2OD2OBpKyxa5ps4ekKAlffZn4P+p/sVfTPxL9EERFYSKO0TSs9P0p4Iz0/SVz3y3Yt1NB2U/wDHTluxbqMP7Kf+OvOuzl5yXUuG2bfm+ht3GHrnRQUtKwua2oe+SXafGbCGZWH0Znh39QKKsExCKnZO7UtmqnaplMZYo5oogXEyv1T7h8xGVrbtNruX3pnp7VYqYPC46ZghLw10LJGkazL5WeRwy3aFaYFUNiqqeV7nMbHNHIXtY2RzQxwcHNY7xXkEDYVZLKylQtPdTW/e2l38uHHkUTTVw533vU9zwk33bsPj395lNN4mNkpgY4oao0rDXRQtayOOoLn2BjZ4scpi1Zc1uwE+tTFwC4q+fC9VIS40kzoGEm/tRa2SNvqaHFo9DQoCr5Q6WaTMXB0kj87wA5wc9zs7xzE3uVm9BeEmrwpjooIKaSB8+tfrBIJXizWkNe1+Vlw3Ycptfcdy1X+j51rWNKCzJb+Xrx6JG7Qd2oXsqr3Rw0/24d+7LOprq1xWtjp4ZZ5XBkcMb5HuO4NY0uJWqTacwz4LVYpQOa98ED3mKUbYpWAExTMa4G/qNiLEGygTTHhAxLFWaqokYyC4cYKdhjjc5u0GS7i+Sx22JtcA2uFWtH6GrXU2uCTxLPFfQvN3pKnRjzbWVg1Vzrkndck26L7bKiIvS0sLBTW8hS1xcdJRBUy4bI6zKv26C52CojbZ7PW+NoP9j6VEq9qGpfDLHNG4skikZJG8b2vY4Oa4eogLj0haK6oSpPv4evcdFrXdGopr/F3m5Y9TnTLhAFG/2zCdHA4TN8Uxvkp5Y/CYyPjS1gjhc0746N24rqMLmDiTVGsxDSiWYWqal9FUEkEZmvqsW1zmA/B1xsfTlvuXTkztnrXlUo6raZek8rJ5PNyqKihPhI4w9BQ1H5Pwilkx3EM5jy0ziKZsrfKiZLEx8lVKLG7YmEbCC4EELBkm1FzW/jB6QYa5smPaLTU1G8ga1sdZSOaCdgDqtjopZNo8Quj+Zb9T8YLRmWmE0VVUS1Dh7XhrKOodiMsnUxwtaWPd/SDyznzWQGh8YP2zTrQ+KlIFWyShfMR5Xgv5UL7O/o6qOu+ZxV7xhdIhU17KKN14qFpD7HYamUNLx6cjAxvoJeFp2iVdWVOLYhpvi0OpdG6WjwOjeS4Gq1bqYCP49PSwOlzvtZ0sj7Wc0gYaeV0j3yPcXve5z3vdtc57yXOcTzkkk/OrX7M2DnUdxLgty833v6L9SC0zdYj7pd/H0PgrKYfRlzmRsbmlkc1jRzue9wa1ov0kgLFPdYX6LH5htKy7HbiDusQQbWttBBCuNZvguRRtJ1HFRXc+Jn63RprGVWprYqiehGaqgZFKwMa14ildDM/xZ2seQCbN2bRfnvOCvA8Mr6t1JXRPLpWF9PJHM+PxmC743NabG7buB/oHpFrGSfwfD42xskEuImU1M745ADBDK3VwQSOGV7XPGd7m3N8rTa1lc8FcL34xQ5DlySOkc74rGxvzHbzbQPnULVlU+zVG5vdnD4Pdx4Y3ZTx5GbOUYXlJQjxxrR3tYb3ce/Vaz58CWuRzA+pqPrU34pyOYH1NR9am/Fb3/bfupt6791UvaV18yXVnpf2Gh4F0NE5HMD6mo+tTfinI5gfU1H1qb8Vve3rv3U29d+6m07r5kurH2Gh4F0NE5HMD6mo+tTfinI5gfU1H1qX8Vvf9t+6vSONwNy8kdFh/gm07r5kurH2Gh4F0IQ4S+CKKlppazDXy2gYZJqaV2svE0Xe+KS2bM0AktN7gGxG45jixe4a75Y37CNStiIBhlB2gxSAjpGUqJ+K97grflbfu8akJX1W40fUjUedWUcN8d+f4ONWtOjdwcFjKe4l1LKqKtkuUsllVEB5mFhNy1pPSQCdm7avuyqiwZPl7A4WcAR0EXH0FA0dC+kTAKWSyqiyYKWRVRAEREMhERAEREAXOvGc0uqfChhMT3RU0cLJagMcQah8ty1kmXaYmtAOXcS4k3sLdFLlPjJj/AOYJ/TTUv2ZCktFwUq2/kyO0nNxo7uaMvT8Ale+hFQauFtW6PWCiMRygluYROqtZYSbh5Fgee21RA4EGxBBGwgixBG8EcxUyU3D7WMoRAaON1a2MRirMp1ZcBlEzqfJtfzluaxPQNihtxJJJJJJuSTcknaSTzm6nbT3+Ze++hC3PuMR919SiIi7TjL+ixSZkXg2tf4KZRMYC46sShr2CVrTsa/LI4Ei1wdt7C1+sE1Z1S+jJfdcfqc9fe0wiIpU5yiIiH0UI6doXzGXs8iQgczSA5o9V9oHqK+iqL5lCMuJiUVJYayfLw538o8vHxdjWjo2NG357r6RFmMFHgEklhLBdUGIzwCZsUjmNqInQTtHkyxP3te07DY7Qd4O7nVqiIoRTbS3vifTk3uYREX0YCIiAutDsZqMIxGlrqARmoiE9NNRTStgjxegq6h1XJT088rhFHikVVJNIxjy0ytmYGFxjex0/Hh00aaxzqqtloZmNBko62hroKyI28gw6gmQ/0oy5p5iRtXOk0bXtcx7WvY4FrmuAc1zTvDmnYQsbT6NYeyRsgo4TlcHat2fUusb5XRNeGlp5x6VUdI+zcqlR1KLW/Lw/5J+00xGEFGonu3ZRIumPCPi+m1S7A9F4ZqbDzYV+Iy3ic6Ek31z2E+C0rgNkYJll3WaMzTM/BDwXYbo3TiOlYJat7AKqvka3XzneWM6inB3RN2c5Ljdx1Dg84VMKo6eOkfhzMMjZc2w+FvguY+U/UstIxx9Tz0lbuOFHAbX8PG69vB6vN+rqb3VZq6MuqctWVN/RZXVZJmF9QmsqS+u43CaJr2uY9rXseC1zHtDmuaRYtc12xwI5io70k0G0UwyOaunw6np2G4khhfPFBVOd/sjQRStp6lzviuYRvJ2AlY/SThroomltBDLVSW8WSUGCBp6SHe2v9Vm+sKGtKNIqzE5tfWSmRwuGMHixRNO9sUe5g2D0mwuSpLR/s9XrSTqrVj58X6L+TkutLU6axT3v8ke2mukkuJ1Gtc1sMETRDSUrLCKmp27GRsa0BodYC5A5gNwAGDRFfqNGFKChBYS3Iq1SpKcnKXFhfcFS6MZSC9nNbym+ix8pv7fWvhFmdNSOepTjUjqyW43nDNEsXrI6fJTTuhLGvgMr7RMjmAkzMDneI11w42CmDgx0HbhbHSzMMtXKAHOABbG3fkZf07zzrDcDnCLRuooKCtmZTVFMxsMb5nBkU0LBliLZHeKJA2zS0nba43m0nUOJ005LYKiCYtFyIpWSEC9rkMJsLrzzSt5d5lRqLVSfcsJ+ZZtE6Ks6TVaD1pY73nHlg+vF6k/qhPF6g/qhXE8zWNc97msY0FznOIa1oG0lzjsA9Kx3shoP57SfWYf8yhFFvgiwOSXFl5FG03vGG+sDavTUM+I36Asf7IaD+e0n1mH/ADJ7IaD+e0n1mH/Ms+7nyZjXjzRkDAz4rfoC9AsX7IaD+e0n1mH/ADJ7IaD+e0n1mH/Mnu58mNePNF9X/wAlL/w3/ulRNxXvcFb8rZ93jWU4UOEmjpaSaCkqI6ismjdEzUOEjIM4LTLI9t2gtBuG3uTbcLlYziwi1DXD/wC8b9hGpWFvUp6PqTksJyhjPln+SOlWhO7hGLzhSyS6iIoElQiIgCIiAIiIAiIgCIiAIiIAiIgKErS9LeE/BsMkMNRVZ52mz4adjp3sPRIW+LG7+i4g+hYbjBaayYVQsgpXllZXF8bHjyoYGBuumb0P8djGnmLyfgrlgnn3nnJ2kk7SSecqXsdHKsteb3dxFXukPdS1IcTqA8PGB/Frj/yzf4q1bSPTbQvEZzU1lDWzzuaxhkLZWEtYLNGWOpDdg9CghFJw0XSi8xbXoyNlpKpJYkk/oTL+WNAfNdb9NT3tfLsY0A82V3zGp74ocui2/YY+KX/sa/tj8Mehf6RSUr6updRMdHSOmkNMx987ISfEa7M5xuB0kqwRF2JYWDkby8lWqQNFtEZsQhdNHLFG1khiIkz3JDGPJ8VpFrPH0FR+1TnwOj/QZvlb/sYFD+0ml7jRejJ3Fs0pa0FlrO5t53MkdD2VO7u40qvDD8jXqjg2rWtJZLTyOG5gdI0n0Aublv6yFqNbSSQSOimY6ORhs5jhYjo9YI2gjYV0ItQ4UcFbPSmpaPbqUZrje+G/tjD0ht84/Nd0qqeyv/Em6rXkbe/w4zaSklquLfDPc1ncTumPZajCi6lvnKWWm85X8kRIiL3AoRQqiqVREAiIsgIiIAiIgCIiAIqEqhcEyZwz6ReZkCrrAsayM4Z9ovPWBV1gTWQwz7RUDgqrJhhERDAUq8W2aOKsxCWRzY446IPe95DWsY2UFznOOwAAH6FFS9YqiRjJGNe5rJQ0SNBIEgY7M0PA8oB223SB0Ljv7V3NCVJPGcb/AKpm+1r+5qKfIkDhc4R34o91LSl0eHsdtO1r6tw+HIN7YQdrWHoBO2wbHNh0BVRfdpaU7amqdNbv1835mK9edaWtN7ymUdATKOgKqLpNJTKOgKoA6B9CKoQBT7xYvcNd8sH2EagNT5xYvcNd8sH2Ear/ALS/BP1X6kpob4lejJcREXmxcQiIgCIiAIiIAiIgCIiAIiIAqKqIDlbjLYgZsdfFfxaSlp4QOh0jTUu+c69v0BRmt54fL+yPE7/GpberwGlstGVzs44ox9EVC7ea0vVhEUlcFfBPPjEfhdRKaSguQx4aHTVGU2eYg7xWRggjO6+0bAdpWytXhRjrTeEfFKjKrLVgiNUsujXcE2jUfiukrpSNhcJxYnnPiRhv0KnJfoz8XED/AG5/BQEvazRsXh1F+X8kpHQN3Leo/wCdDnOyLo3kx0Y6uvP9u5fcHBvouxwcaWskt8F9RLlPrDZGm3zr4fthoz5iPrs/d+H/ADoc8UdI9/jhrtW17WvkscjXODnNYXbg8hjiBv8AFPQp44LqN0OHMLhYzyPnAO8NeGMZ8xbGHf1lsWk2D4dU0tNQwUraekp6ltTqomtjbK5sUsWV4btIOtuTe5y79q9mgAAAAACwAFgANwAG4Kk+2Htdb31mrO335lrSfdu/Clz45f0LDoPQdS2rutU5YS9eLC+KiISMex3kvY5jh/RcC0/sK+0XmkZOLTXFFtkk1hnOaIi/ah4QFSyqqICiIUWQEREAREQBUVUKBFtO8hWr5Sr+SO68XU65K1Ob4M6YTiuJYmYprSrw0yp4L/7suV0KvM3qrT5FprSvtspVwKZfQpllUKvMw6sOR5xPKvIzsXmyCy9gF20YSXE5qkk+BVERbzSEREAREQBERAF9KgVVgBT5xYvcNd8sH2EagNT5xYvcNd8sb9hGoD2l+CfqiU0N8SvRkuIqIvNi4lUVEQFUVEQFUVEQFUVEQFUVEQFUVEQFVRERA5W4ylCYsekktYVVNTTA9JYw0x+f2gKNF01xlNEX11DHW07C+fDy8vY3a59JJYy2Hwiwsa+3RrFzKrdo6sp0V5biraQpOFZ+e898OpjPNDADYzTRQg9Ble1gP/UuvMdyU0cFDANXBBCxjWt2DKwBkbdnMA1coaIC+I4cOmvoh/8AsxLqvSz3U/8ANZ/cqZ/xBuJ07SKi+Lw/QnfZanGVWTa4GJREXi5fwiIhgIiIAvOrnbFG+R3kxsfI781jS4/sC9Fo/Cpj7Y4TRRuvNMBrrf7OHyi09Dn7Bb4ubpCl9BaJq6TvKdtTXFrL5R72/ocOkb2FpQlVk+C3eb7kRWiIv18eLBERAUsqFfSpZAUREWQEREAREQBUsqogFkREGQiIgyEREAREQBERAEREAVQqBfSwwEREAUi8E/CHDgtPUQy00s5mnEodG9jQ0CNrMpDufxf2qOlULmu7Snc0/d1OHQ20K86MteHEnfl2pPN9T2sSry7Unm+p7WJQOiiuzdl4X1Z27Yuea6E8cu1J5vqe1iTl2pPN9T2sSgdE7N2XhfVjbFzzXQnjl2pPN9T2sScu1J5vqe1iUDonZuy8L6sbYuea6E8cu1J5vqe1iTl2pPN9T2sSgdE7N2XhfVjbFzzXQnjl2pPN9T2sScu1J5vqe1iUDonZuy8L6sbYuea6E8cu1J5vqe1iTl2pPN9T2sSgdE7N2XhfVjbFzzXQnjl2pPN9T2sScu1J5vqe1iUDonZuy8L6sbYuea6E8cu1J5vqe1iTl2pPN9T2sSgdE7N2XhfVjbFzzXQnc8OtJ5vqe1iUa6V1mjldK6dmH19DI8lzxSzUwie4m5dqZGlrD+ZlC1JFsp6AtabzBNejZrqaTrVFieH9DN4UzBaeop6hrcWc6nnhna10lFlc6GRsjWutHfKS0DYpqotI2YqwVscT4WvJYGPLXOBjJYTduzbZc9KYuCv3tj/4s/2hXn//ABN0fSo6MjUjnPvIre87sSLF7JXM5Xbhuxqt/obSiIvBD0gt34mWkt/JdXKASNY2so2NfbZna17szQd9jt2r4OMEf/R6369Qf51drUNL9Nm0M4p2Qa97Q10pMmrazMA5rRZpJdlIPRtG9W3Q7q6QqK3trSnOWM963Li29ZL+7IO91LWPvKtaSWfLpwMlpDite+NooMLlhl1gL3VNTSSsMWV4LWtjlBD82Q3vuB6VoHKJiJ5qYf2Lv8ZFJ2A4kysp46iMENkB8V29rmuLXNNt9iDt51AS9C9hrCz0jO4o3lpTUqbS4POXrZTy3yK17QXNa2VOpRrSakm+PduxwS5mz1mneJSNLdayO+90UbWu+ZxuW+sWK1qR5cS5xLnE3c4kkuJ3kk7ST0r5Req2OibOxTVtSjDPHVWMlPuLytcf82bl6soigjlkxPqKDsqjvCcsmJ9RQdlUd4XD2ks+b6Hdsa45LqTuigjlkxPqKDsqjvCcsmJ9RQdlUd4TtJZ830GxrjkupO6ooJ5ZMT6ig7Ko7wnLJifUUHZVHeE7SWfN9Bsa45LqTsqKCuWTE+ooOyqO8KnLHifUUHZVHeFntJZ830GxrjkupOyKCeWPE+ooOyqO8Jyx4n1FB2VR3hO0tnzfQbGuOS6k7IoJ5Y8T6ig7Ko7wnLHifUUHZVHeE7S2fN9Bsa45LqTsignljxPqKDsqjvCcseJ9RQdlUd4TtLZ830GxrjkupOyKCeWPE+ooOyqO8Jyx4n1FB2VR3hO0tnzfQbGuOS6k7IoJ5Y8T6ig7Ko7wnLHifUUHZVHeE7S2fN9Bsa45LqTsignljxPqKDsqjvCcseJ9RQdlUd4TtLZ830GxrjkupOyKCeWPE+ooOyqO8Jyx4n1FB2VR3hO0tnzfQbGuOS6k7IoJ5Y8T6ig7Ko7wnLHifUUHZVHeE7S2fN9Bsa45LqTsq2UEcseJ9RQdlUd4VeWTE+ooOyqO8J2ks+b6DY1xyXUnZVUEcsmJ9RQdlUd4TlkxPqKDsqjvCx2ks+b6DY1xyXUndFBHLJifUUHZVHeE5ZMT6ig7Ko7wnaSz5voNjXHJdSd1UKB+WTE+ooOyqO8JyyYp1FB2VR3hO0lnzfQbGuOS6k8IoH5ZMU6ig7Ko7wnLJinUUHZVHeE7SWfN9DGxrjkupPCKB+WTFOooOyqO8JyyYp1FB2VR3hO0lnzfQbGuOS6k8IoH5ZMU6ig7Ko7wnLJinUUHZVHeE7SWfN9Bsa45LqTwigflkxTqKDsqjvCcsmKdRQdlUd4TtJZ830GxrjkupPCKB+WTFOooOyqO8JyyYp1FB2VR3hO0lnzfQbGuOS6k8IoH5ZMU6ig7Ko7wnLJinUUHZVHeE7SWfN9Bsa45LqTwigflkxTqKDsqjvCcsmKdRQdlUd4TtJZ830GxrjkupPCKB+WTFOooOyqO8JyyYp1FB2VR3hO0lnzfQbGuOS6k8IoH5ZMU6ig7Ko7wnLJinUUHZVHeE7SWfN9Bsa45LqTwpB4LNI44gaKdwYHPL4HuIDbutmjcT5NzYgnnJHRfkblkxTqKDsqjvCcsmKdRQdlUd4UNp650bpe0la1nJJ700t6a4P8AzuO7R1peWVZVoJbu7PFd6P0DRcK4TxjNIqUBsRo8g2CN8U8jAOhoknOQehtlmmcazSIDbSYM70mmrQT+rWgLxG79j7inPFGcZrn+F/VP+WX6hpaEo/6kWn1O0FqmlmhUVdMJxM6CQhrZLMEjXhos02zAh1rC99wC5b/8VukX8ywX6vXd+VpXcaLSWQWbHhkI/wB1TTX+YyVDiF06H0NpawuPfW81TeMZznc+Kxh5NN9c2lzT1KkXJcccDrmuqqXBqFrAfIa4QsJBkmkJLiT6MziSdwHzBQqoMquG3F5XmSWOjked73sqXuPou6pvb0Ly5ZMU6ig7Ko7wvVPZZWeiIVJVKkp1aj1pyxjL38OrKhpalc3soqMFGEViKyTwigflkxTqKDsqjvCcsmKdRQdlUd4Vq7SWfN9CI2Nccl1I3REXnhbwiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiA//2Q==" width="302px" alt="education chatbot examples"/></p>
<p><p>By answering queries related to the date, time, subtopics covered, and speakers in a timely manner, these bots make it easier for students to develop their interest in the event. Not only this, a free chatbot for education also makes it easier to enroll through a seamless registration process, apart from timely reminders and updates about the event. Chatbots have become a staple in education, helping both students and teachers in a bunch of ways. When it comes to students, an AI-based bot can offer on-the-go feedback and create personalized learning plans. It supports students by providing information on admissions, course details, financial aid, campus services, and academic resources. Having an integrated chatbot and CRM can streamline the application process for prospective students.</p>
</p>
<p><p>During holiday periods, when learners might face difficulties reaching teachers, chatbots become valuable tools for assistance. They facilitate communication of homework details, schedules, and answer queries. Furthermore, they aid in conducting assessments, even in courses requiring subjective evaluations. These bots offer individualized support to learners, providing guidance, and aiding in workload management for both teachers and educatee. By streamlining routine activities, chatbots help pedagogues focus on delivering high-quality knowledge and monitoring attendees’ progress.</p>
</p>
<p><p>An AI-enabled education chatbot can deliver personalized communication and nudge the student to act faster. The chatbot can not only explain the steps involved, but also save the counselor’s time on following-up for necessary documents. The solution is to integrate an education chatbot with a higher-education CRM to help your admissions team create magic. Education chatbots aid the admissions process in many ways —decrease student drop-offs, shorter response times, automated follow-up reminders, and faster query resolution.</p>
</p>
<p><p>Pounce helped GSU go beyond industry standards in terms of complete admissions cycles. In today’s digitally driven world, technological advancements continue to reshape various industries, and higher education is no exception. Students can better understand and retain the material when offered such continuous support in learning. Understanding student sentiments during and after the sessions is very important for teachers.</p>
</p>
<p><p>Chatbots equip institutions to meet the challenges of today&#8217;s digital world and prepare for the future of education, which promises even greater integration of AI technologies. Conversation-based approach helps build confidence and fluency, providing learners with a more interactive and engaging way to practice languages compared to traditional study methods. Ongoing feedback allows institutions to make agile adjustments to their educational offerings, enhancing the quality of education. Some chatbots have options to opt out of sharing data which are described  in the terms of service.</p>
</p>
<p><p>Also, imagine the usefulness of chatbots in times of crises, when parents and students have loads of questions that can overwhelm school employees. Such is the case of the 2020 pandemic when schools may slowly reopen and many parents are concerned about the dangers. As <a href="https://www.metadialog.com/blog/chatbots-in-education-sector/">education chatbot examples</a> students get back to the classroom, questions about health and safety measures, school hours, and protective gear are likely to rise in numbers. Capacity is an AI-powered support automation platform that offers a low-code platform accessible through conversational AI.</p>
</p>
<p><p>The team can then take data-driven decisions by identifying trends, optimizing recruitment strategies, and allocating resources effectively. In this article, we discuss how you can leverage chatbots to improve university enrollments, automate administrative tasks, and personalize student interactions. Educational institutions can start by identifying areas where chatbots could have the most impact, such as customer service, admissions, or student support. ChatBot offers the University Template that can be customized to meet specific needs.</p></p>
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		<title>Best Free AI Tools 2024 Ultimate List of AI Software &#038; Websites AI Online Free</title>
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		<dc:creator><![CDATA[prith001]]></dc:creator>
		<pubDate>Mon, 29 Jan 2024 11:24:45 +0000</pubDate>
				<category><![CDATA[Artifical Intelligence]]></category>
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					<description><![CDATA[5 Best AI Tool Aggregators Thousands of AI tools have been by René Remsik The platform adjusts to individual learners&#8217; needs by offering customized content and feedback. This helps students progress at their own pace while offering educators tools to monitor student progress and adjust their teaching methods accordingly. First on our list is TensorFlow, &#8230;<p class="read-more"> <a class="" href="https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?p=13447"> <span class="screen-reader-text">Best Free AI Tools 2024 Ultimate List of AI Software &#038; Websites AI Online Free</span> Read More &#187;</a></p>]]></description>
										<content:encoded><![CDATA[<p><h1>5 Best AI Tool Aggregators  Thousands of AI tools have been by René Remsik</h1>
</p>
<p><img decoding="async" class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' 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" width="300px" alt="ai tool aggregator"/></p>
<p><p>The platform adjusts to individual learners&#8217; needs by offering customized content and feedback. This helps students progress at their own pace while offering educators tools to monitor student progress and adjust their teaching methods accordingly. First on our list is TensorFlow, an open-source artificial intelligence machine learning platform developed by Google back in 2015. The platform has gained significant popularity and widespread adoption for providing a comprehensive framework for building and deploying various types of machine learning models.</p>
</p>
<p><p>Magic School is designed to improve learning and education through interactive tools, automated processes, and innovative features. It offers a variety of tools to create customized educational content, such as quizzes, exercises, and multimedia presentations. It can even focus on personalized learning paths and adapt content to individual learners&#8217; needs.</p>
</p>
<p><p>Before you can start prospecting, you need to setup a sales campaign the right way. We hope to partner with more cities in the future to inform their cooling strategies and ultimately create safer, healthier and more sustainable communities. The exact contents of X’s (now permanent) undertaking with the DPC have not been made public, but it’s assumed the agreement limits how it can use people’s data.</p>
</p>
<p><p>The tool harnesses the power of artificial intelligence to offer a range of features, enhance productivity, and simplify daily tasks. For starters, it leverages advanced natural language processing and machine learning algorithms to understand user commands and respond accordingly. Lovo.ai is a text-to-speech (TTS) software that provides AI-generated voices in multiple languages and accents. It uses advanced deep-learning technology to produce natural-sounding voices with expressiveness and emotion. You can use it to create custom voiceovers for a variety of applications, including podcasts, e-learning courses, videos, and virtual assistants. In the ever-evolving realm of artificial intelligence, AI Aggregators have emerged as a beacon of seamless integration.</p>
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<p><p>Leonardo and Topaz Labs use AI to generate stunning images and improve existing visuals, giving your content a polished and professional look that stands out in a crowded digital space. QByte offers AI-enhanced tools for asset management and maintenance optimization. Automate web scraping using natural language with AgentQL, enhancing data extraction.</p>
</p>
<p><p>The platform uses AI algorithms to scan charts and identify various chart patterns, such as triangles, double tops, and more. This functionality helps traders save time by automating the process of pattern identification and enables them to focus on analyzing and executing trades. Remember that Zapier integrates with numerous applications, so the possibilities for automation in marketing and sales are extensive. You can explore the Zapier website and search for specific integrations to find the apps that best suit your sales and marketing needs. The color enhancement feature enhances the colors in an image, boosting vibrancy, saturation, and contrast. It adds depth and liveliness to dull or faded photos, making them visually striking.</p>
</p>
<p><p>It uses a simple questionnaire to understand your style and preferences, then generates logos, color schemes, and other brand assets. For busy founders, it&#8217;s a quick way to get a professional look without hiring a designer. Entrepreneurs, freelancers and aspiring thought leaders need to get involved, and the right tools can make a big difference.</p>
</p>
<p><p>Access powerful AI online for free and elevate your tech journey with the latest in AI innovations. AI tools can significantly enhance your business operations by automating tasks, providing insights, improving customer interactions, and boosting overall productivity. They can gather <a href="https://chat.openai.com/">https://chat.openai.com/</a> insights, generate reports, and predict trends by using various AI models present in the aggregator. Furthermore, for e-commerce portals, an integrated AI model can assist in everything from chatbot customer service to product recommendation, thus enhancing the user journey.</p>
</p>
<p><p>Alexa&#8217;s strength mainly lies in its ability to understand and respond to natural language commands. It leverages automatic speech recognition (ASR) and natural language understanding (NLU) algorithms to process user queries accurately. This ability allows users to engage in conversations <a href="https://play.google.com/store/apps/datasafety?id=pl.edu.pg.chatpg&amp;hl=cs&amp;gl=US">Chat GPT</a> with the assistant, asking questions, giving commands, and even engaging in natural back-and-forth interactions. You can easily access information from Google Search, retrieve personalized updates from Google News, control Google Photos, and even interact with third-party applications.</p>
</p>
<p><p>Training the model to look both at specific sections of an image and the whole image allowed it to relate specific changes in one region to the overall context. This approach, the researchers said, enabled CHIEF to interpret an image more holistically by considering a broader context, instead of just focusing on a particular region. TopAI.tools is renowned as one of the premier AI tool aggregators and search engines, serving as a comprehensive repository in the AI space. What sets TopAI.tools apart is its AI-powered search bar, enabling users to swiftly locate the perfect tool for any task at any time.</p>
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<p><p>The AI-powered chatbots also come in handy to handle routine customer queries, freeing up more of your time to focus on more important issues. In addition to that, the library of sounds and instruments on the platform is not so extensive compared to its alternatives. However, it still makes a good option for beginners who are just getting started with AI music generators, or those simply looking for some inspiration.</p>
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<p><p>In essence, The Biggest AI Aggregator is the quintessential resource for anyone delving into the realm of AI technology. Its vast database, coupled with an intuitive search interface and consistent updates, transforms complex tasks into effortless achievements. Each tool profile provides details on features, pricing, supported platforms, and reviews. While the directory could use more tools, the focus on pricing makes it a valuable option.</p>
</p>
<p><h2>AI Tools Directory</h2>
</p>
<p><p>Users can then apply various styles like cubist or impressionist to their generated images using the Style Transfer feature. One feature that creators look for in these tools is video templates, which wave.video has provided in abundance. You get more than 300 templates, each designed differently to generate videos with different styles and for different purposes. The interface is intuitive and user-friendly, which allows easy navigation and music creation. Even newbies can easily customize their music tracks to suit their specific needs.</p>
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<p><p>Sarvam&nbsp;is a Bangalore-based AI startup creating voice-based large language models tailored to India&#8217;s diverse linguistic and cultural landscape. The company specializes in building generative AI models that include speech-to-text, text-to-speech, contextual translation and advanced data parsing systems, all designed for use in regional languages. Sarvam&nbsp;recently announced a partnership with Microsoft to scale its tech through Azure and encourage the widespread adoption of AI across India. The course covers a wide range of topics, starting with an introduction to AI and its historical context, to exploring various AI technologies, such as machine learning, deep learning, and natural language processing. It delves into ethical considerations, societal impacts, and the future of AI, providing a foundational understanding of AI concepts and giving participants a holistic view of the AI landscape. The online learning platform intuitively provides a seamless learning experience, allowing participants to access lectures, interact with the instructors and fellow learners, and complete quizzes and assignments with ease.</p>
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<p><p>It also includes automatic differentiation, a critical feature for optimizing models through techniques such as gradient descent. The open-source nature encourages collaboration and innovation, enabling researchers and practitioners to share their models, techniques, and best practices. This vibrant community has greatly contributed to the growth of TensorFlow, making it one of the most widely used platforms for AI-driven machine learning. Secondly, its integration with all the other Microsoft services comes in handy in streamlining workflows, saving time, and enhancing productivity and efficiency.</p>
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<p><p>NeuralStyler is available on multiple platforms including Windows, Mac, and Linux, making it easily accessible to almost everyone. The style transfer feature comes with multiple style image functionalities that allow users to choose any style image they want from the library, and apply it to their own image. Other features include dreamlike visuals and interpretations of patterns, which interpret and enhance existing images to generate an output that is surreal and dreamlike, featuring strange, abstract shapes and patterns. In addition to templates, the platform has over 200 million stock videos and images, making it easy and fast to find the right footage and images for any project.</p>
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<p><p>The premium pricing is a stark pivot for Canva, which was once considered to be a simple and affordable alternative to more expensive graphic design software provided by Adobe. Canva users online have condemned the increases, with some announcing they’ll be canceling their subscriptions and moving to Adobe applications. For agencies, AI analytics translates to better targeting, improved conversion rates, and superior campaign performance. By making decisions based on data instead of relying on the agency’s own instincts, the agencies can guarantee that their marketing strategies are not only cost-effective but also provide the best results for their clients. By adopting finetuned AI transcription tools like Transkriptor, agencies can be in a position to reduce the cost and time used in transcription while at the same time being assured of the accuracy of the transcription.</p>
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<p><p>Our AI tools list is regularly updated to ensure you have access to the latest and most effective AI tools available. These lists can be exported and shared among teams or used to facilitate side-by-side comparisons of various AI tools. For AI app and tool creators, TopAItools offers an exceptional opportunity for visibility and promotion. Futurepedia.io stands as one of the most extensive AI tool aggregators, offering a vast collection of thousands of innovative solutions spread across over 50 diverse categories. We prepared a list of the coolest and largest AI tool aggregators, where you can find thousands of AI tools, AI news, and much more.</p>
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<p><h2>Improve Design Efficiency with AI Design Tools</h2>
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<p><p>These tools are very useful in any agency because of the flexibility that comes with them when it comes to content creation. In this post, we&#8217;ll look at 8 of the best AI sales tools, focusing on their features, pros, and cons. Interestingly, the AI also analyzes more than two billion top-performing videos on YouTube made by similar creators to offer Spotter users recommendations on how to boost their own videos. The feature, called “Outliers,” acts as a “research copilot,” taking videos from other YouTubers that a creator’s audience is also watching. Users can click on a title, and Spotter’s AI tool will brainstorm ideas for their channel. However, Spotter Studio claims to differ from other tools because its solution is more tailored to individual preferences.</p>
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<p><p>One of the best features of Boomy is its ability to create high-quality music in different styles. The platform offers a variety of options in genres, moods, and instruments that you can choose from, making it possible to create a wide range of music styles. It uses artificial intelligence to generate music based on a user&#8217;s inputs such as genre, mood, and tempo. You can then easily customize the music to fit your needs by adjusting the length, intensity, and instrumentation of the track.</p>
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<h3>8 top generative AI tool categories for 2024 &#8211; TechTarget</h3>
<p>8 top generative AI tool categories for 2024.</p>
<p>Posted: Wed, 31 Jan 2024 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiiwFBVV95cUxNdEJueE9hZ0k2a25tZVpGRE1RVEZ3VTZYejJrR25obUpjeXkxUElmZVhUY3pvTVFPTmVHc3J6cEF2NHNLeEdscE9pT004Z0Vfa0dDTW5tQ0JsT0NEZHl5b0JnN0tOaDRYdll4a1dJZUpHelA4cEMxcWFTamhrTGxtc09mcF9mTU5Ibi1B?oc=5' rel="nofollow">source</a>]</p>
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<p><p>The more the user interacts with the software, the more the AI algorithm learns about their preferences and generates personalized images. You can easily create product demos, explainer videos, social media ads, and more. Once you select a template, you can customize it with your own texts, images, and videos, or upload your own content to create a completely unique video. Using machine learning techniques, it will then create lifelike character animations and backgrounds based on the generated scene descriptions. The final process involves combining the generated scenes, characters, and animations with a voiceover or soundtrack to produce a finished video.</p>
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<p><p>Find which ICPs are worth targeting, then use our AI-powered database to double down on the markets that have the biggest appetite for your product or services. With a database and a cold outreach tool ⏤ provided you&#8217;ve got the right configuration ⏤ you can potentially scale this and include personalization. To lower city temperatures and keep communities healthy, Google Research is continuing its efforts to use AI to build tools that help address extreme heat. Additionally, Spotter Studio’s “Projects” tool acts as an all-in-one project planner for organizing tasks and collaborating with teams. It also tracks projects throughout the different stages, from in development, post-production, ready to publish and published.</p>
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<p><h2>Boost Productivity. Embrace AI.</h2>
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<p><p>CHIEF achieved nearly 94 percent accuracy in cancer detection and significantly outperformed current AI approaches across 15 datasets containing 11 cancer types. In five biopsy datasets collected from independent cohorts, CHIEF achieved 96 percent accuracy across multiple cancer types including esophagus, stomach, colon, and prostate. When the researchers tested CHIEF on previously unseen slides from surgically removed tumors of the colon, lung, breast, endometrium, and cervix, the model performed with more than 90 percent accuracy.</p>
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<p><p>All you need to do is select a template for the type of video you want to create, input additional media like text and images, and the system will do the rest for you. This helps you increase productivity and consistency in content creation, as you don&#8217;t have to keep writing your content from scratch for your different social platforms. It has a user-friendly interface and a clean and modern design with easy navigation. This level of flexibility makes Heyday suitable for businesses of all kinds and sizes.</p>
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<p><p>Although an unexpected answer from an executive, his statement is unfortunately correct. Replicating something that’s already successful has been done for decades and will likely never stop. Plus, while YouTube videos themselves are protected by copyright, the underlying idea and concept aren’t.</p>
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<p><p>AI Parabellum is an AI tools directory that provides a curated list of top-notch Artificial Intelligence tools designed to enhance various aspects of your work and creativity. Harness the power of smart AI search to pinpoint the ideal tools for any use case. Whether you’re identifying the best AI tool for a specific task, comparing various AI options for a particular project, or exploring new possibilities for your next endeavor, Theresanaiforthat has you covered.</p>
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<p><p>The effectiveness of Stock Hero will ultimately depend on market conditions, the user&#8217;s chosen strategies, and the quality and timeliness of the data it receives. We, therefore, recommended for users to thoroughly backtest and validate strategies using historical data before deploying them in live trading. This AI-powered platform is designed to help you grow and manage your social media pages faster and with ease. It uses advanced AI algorithms to empower marketers to create engaging and original content fast and easily.</p>
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<p><p>We recommend that you always verify the information from reliable sources when needed. Explore the diverse ecosystem of aggregators that bring together top AI tools from various domains, making it easy to find the right tool for your projects. These platforms offer a centralized hub for accessing various innovative AI solutions. While the directory size is more modest, TopTools AI is a well-designed option for quickly scanning options within technical categories.</p>
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<p><p>These intelligent software leverage natural language processing (NLP) algorithms and machine learning techniques to understand and respond to user input. They can analyze users’ messages, interpret the intent behind the messages, and generate appropriate human-like responses, allowing for more engaging interactions with users. The price of some Canva subscriptions are set to skyrocket next year following the company’s aggressive rollout of generative AI features. Global customers for Canva Teams — a business-orientated subscription that supports adding multiple users — can expect prices to increase by just over 300 percent in some instances. Canva says the increase is justified due to the “expanded product experience” and value that generative AI tools have added to the platform.</p>
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<p><p>AI is changing the game, offering new ways to create, manage, and grow your online presence. The team also tested CHIEF on its ability to predict mutations linked with response to FDA-approved targeted therapies across 18 genes spanning 15 anatomic sites. CHIEF attained high accuracy in multiple cancer types, including 96 percent in detecting a mutation in a gene called EZH2 common in a blood cancer called diffuse large B-cell lymphoma. It achieved 89 percent for BRAF gene mutation in thyroid cancer, and 91 percent for NTRK1 gene mutation in head and neck cancers.</p>
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<p><p>The platform’s intelligent AI search feature empowers users to pinpoint precisely what they need, ensuring unparalleled efficiency in their quests for the ideal solution. Futurepedia is a leading AI resource platform, dedicated to empowering professionals across various industries to leverage AI technologies for innovation and growth. In our rapidly evolving technological landscape, AI tools like ChatGPT, Runway ML, Gamma AI, Facecheck ID, Magic School AI, Looka, Uberduck AI &amp; Playground AI are essential for advancement in nearly all areas of business. Our platform offers comprehensive directories, easy-to-follow guides, a weekly newsletter, and an informative YouTube channel, simplifying AI integration into professional practices. Committed to making AI understandable and practical, we provide resources tailored to diverse professional needs, fostering a community of more than 200,000 professionals sharing knowledge and experiences. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.</p>
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<p><p>However, you should be aware that PowerPoint Speaker Coach may process audio data during presentations. Although, Microsoft addresses these concerns by encrypting all the processed data. The  instructors are highly regarded experts in the field of AI, who bring their deep knowledge and experience to the course, presenting the materials in a clear and engaging manner. They effectively break down complex concepts, using relatable examples and real-world case studies to illustrate the potential of AI and its impact on different sectors.</p>
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<p><p>This AI tool aggregator has listed more than 2,200 AI tools and also has an AI news section on its website. Unveiling AI&#8217;s magic with step-by-step tutorials, in-depth reviews and aiwizard spellbook spells. Sign up to our daily newsletter and get the coolest new tools &amp; AI news every day. Aiwizard AI tools directory is going to be powered by the $WIZM (wizard mana) token. Students also get a professional certificate that is downloadable and shareable on their professional social platforms like LinkedIn.</p>
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" width="302px" alt="ai tool aggregator"/></p>
<p><p>Synthesia is another powerful tool for anyone looking to create engaging and professional videos, fast and easily. Moreover, the AI logo maker allows you to design professional logos that effectively represent your brands. Lastly, Hostinger AI Builder&#8217;s AI heatmap tool can help you analyze your website&#8217;s traffic patterns and user behavior, equipping you with the information you need to enhance user experience and engagement. Its AI music generator is another handy feature, which lets you create royalty-free music based on your tips and selected genres. This feature, combined with customizable shortcut keys for easy access to favorite sound effects, further improves Hitpaw’s usability for both beginners and professionals. It also includes AI avatars that sync with voiceovers and make your presentations and sales decks more engaging.</p>
</p>
<p><p>Incorporating audio and video elements into your content can significantly increase its impact and reach. Suno, Otter, and Spotter Labs use AI to streamline the production process, from generating custom music to transcribing presentations and identifying trending topics. By automating these tasks, you can focus on crafting compelling narratives and delivering high-quality content to your audience.</p>
</p>
<p><h2>Best AI Sales Tools</h2>
</p>
<p><p>To compile this list of the top AI tool aggregators, I spent over 20 hours researching online. I began by searching on Google for “AI tool directories” and analyzing the top results. I also checked various AI and tech publications for mentions of popular aggregators. In addition, I consulted with some AI professionals in my network and analyzed social mentions and backlinks to gauge reputation. Some key factors I considered were the number of tools listed, categorization approach, quality of content and resources, design, and user experience. After a thorough review process, these are the top 10 AI tool aggregators that stood out.</p>
</p>
<p><p>First, it provides automated site generation,  allowing users to quickly create a fully functional website in an instant. Next, the platform includes an AI-powered writing assistant and image generator, which can help you come up with compelling content and visuals for your website. Play.ht is a text-to-speech (TTS) service that allows users to convert written text into realistic-sounding audio. The software offers a range of options for users, including male voices, female voices, and multiple languages.</p>
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<p><p>It also explores advanced AI topics, including natural language processing, computer vision, and robotics. This comprehensive approach provides a holistic view of artificial intelligence, ensuring that students gain a deep understanding of the diverse applications and techniques within the field. Adobe has been around for ages, offering multiple features and services to artists, designers, and marketers. One of their best features yet is Sensei, an AI-powered virtual assistant that combines the power of artificial intelligence and machine learning to enhance productivity, automate tasks, and provide intelligent insights.</p>
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<p><p>Perplexity is a newcomer in the world of search engines, but it&#8217;s making waves (and has even been dubbed “the Google killer”). It combines the best of traditional search with AI assistance, giving entrepreneurs quick access to accurate, up-to-date information. Unlike Google, where you might spend time sifting through results, Perplexity serves up concise answers and relevant facts right away.</p>
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<p><p>It also lets you specify aspect ratios, which can range from standard squares to wide cinematic formats. Enhance your productivity and easily drive innovation using the diverse range of tools available on these platforms. Start exploring the possibilities and harness the potential of AI with Aggregators today. AIToolsDirectory maintains a categorized directory of over 1600 AI and machine learning tools. Its strength lies in the breadth of tools covered across industries like healthcare, education, marketing, and more.</p>
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<p><img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' src="https://www.metadialog.com/wp-content/uploads/2022/06/logo.webp" width="307px" alt="ai tool aggregator"/></p>
<p><p>This tool helps you stay current and knowledgeable in your field without spending hours on research (or fact-checking ChatGPT’s responses). By consistently sharing accurate, insightful information, you position yourself as a go-to expert in your industry. It&#8217;s like having a research assistant by your side, helping you build credibility with every post or comment. These AI-powered tools streamline your writing and research processes, saving you valuable time and effort. By automating text generation and providing instant access to relevant information, Claude and Perplexity enable you to focus on higher-level tasks and strategic decision-making.</p>
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<p><p>Favird is a directory of over 1300 AI and machine learning tools categorized by functionality. What sets it apart is the inclusion of detailed reviews and ratings for each tool by users. You can foun additiona information about <a href="https://www.inferse.com/854199/elevate-customer-support-with-cutting-edge-conversational-ai/">ai customer service</a> and artificial intelligence and NLP. This helps provide a more well-rounded perspective beyond just the marketing descriptions.</p>
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<p><p>Marketing through data is crucial for agencies that wish to achieve the best results in their campaigns. AI analytics tools give the ability to process large volumes of data within a short span of time and with high accuracy, which is very important while developing the right marketing strategies. These tools can help to find trends, to predict the behavior of customers, and to suggest the best ways to reach the target groups. AI project management tools also offer predictive analysis, which enables agencies to know what can go wrong before it happens. This allows teams to make changes as necessary and keep projects on schedule and within cost constraints. Lastly, these tools are effective in increasing efficiency while at the same time providing excellent service to the clients through standardization of services.</p>
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<ul>
<li>Once ready, do regular monitoring of your AI tool&#8217;s performance and make improvements as needed.</li>
<li>Like for testing a data analysis tool, we use different datasets to check the accuracy and efficiency of a tool.</li>
<li>Aitrendz.xyz is one of the coolest AI tool aggregators, as it offers AI tools, AI news, lists of AI books, movies, AI influencers, etc.</li>
<li>In today’s fast-paced digital landscape, using AI tools can significantly enhance your productivity, streamline workflows, and generate creative content.</li>
</ul>
<p><p>Several products in this market make great claims that aren&#8217;t backed by the user experience, or crucially, lead generation results. Unlike the other AI sales tools in this list, Dux-Soup is a LinkedIn-based outreach SaaS product. Overloop taps into an advanced AI-powered B2B database containing over 450 million contacts.</p>
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<p><p>You&#8217;ll get features like style suggestions, advanced grammar checks, and plagiarism detection. These features can save you time while working on academic and professional projects. This might <a href="https://www.metadialog.com/blog/artificial-intelligence-ai-for-data-aggregation/">ai tool aggregator</a> be a drawback for some users who only require basic grammar and spell-checking. However, OpenAI Playground can be a little tricky for beginners who don&#8217;t have much coding experience.</p>
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<p><p>It is here that AI transcription tools come in handy since they are capable of transcribing spoken language to written text in a short span of time and with a high level of accuracy. These tools are especially helpful in making subtitles, taking notes of meetings, or converting podcasts into blogs. To agencies, the use of AI writing tools can save much time that would have been used in writing content so that the teams can work on other more important tasks.</p>
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<ul>
<li>The Biggest AI Aggregator stands as a colossal online platform, hosting a staggering array of 6,149 AI tools tailored for over 1,728 distinct tasks.</li>
<li>From text generation to image creation, from music composition to video production, AI Aggregators ensure that the world of AI is at your fingertips.</li>
<li>It offers a video dubbing tool that automatically translates and dubs audio for videos from YouTube or uploaded files.</li>
<li>Word and mouth and referrals, even networking, expos, trade shows, and LinkedIn aren&#8217;t enough.</li>
</ul>
<p><p>The model identified tell-tale patterns on images related to tumor aggressiveness and patient survival. When human pathologists analyzed these AI-derived hot spots, they saw intriguing signals reflecting interactions between cancer cells and surrounding tissues. One such feature was the presence of greater numbers of immune cells in areas of the tumor in longer-term survivors, compared with shorter-term survivors. That finding, Yu noted, makes sense because a greater presence of immune cells may indicate the immune system has been activated to attack the tumor. Discover top-notch artificial intelligence tools, AI software, and AI websites to enhance your digital experience.</p>
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<p><p>This course will kickstart your career in artificial intelligence, introducing you to all things AI, the essentials, and practical applications. The lecturers are accompanied by top-notch learning materials and resources including well-structured slides, lecture notes, and supplementary reading materials, which serve as excellent references for further exploration. It also incorporates hands-on programming assignments and quizzes, allowing students to apply their knowledge and gauge their understanding of the topics covered.</p>
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" width="308px" alt="ai tool aggregator"/></p>
<p><p>If you need strong regional accents or unique speech patterns this might not be your suitable option. Like most of its alternatives, Murf.ai offers a pay-per-use model, which means that you only pay for the number of characters you need to convert to speech. You also get a free trial with 5,000 characters, to get you started with the software and allow you to test the service before committing to a plan. It further shines bright in delivering high-resolution images as it supports outputs up to 4K.</p>
</p>
<p><p>This feedback helps you adjust your speaking style and make improvements for a more effective presentation. It enables developers to construct neural networks and other computational graphs through a flexible and efficient programming interface. It has the ability to handle both numeric and symbolic computations, which allows users to define complex mathematical operations and algorithms. Whether you are new to the concepts of AI or want to advance your AI knowledge, the MITs Artificial Intelligence course is a good option.</p></p>
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