/**
 * 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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			<h1 class="entry-title" itemprop="headline">What is sentiment analysis? Using NLP and ML to extract meaning</h1><div class="entry-meta">			<span class="comments-link">
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			 / <span class="cat-links"><a href="https://985ecb7690064bccb2c34efa3f1ed2e0-devsite.readyship.co/?cat=9" rel="category">Artifical Intelligence</a></span> / By <span class="posted-by vcard author" itemtype="https://schema.org/Person" itemscope="itemscope" itemprop="author">			<a title="View all posts by prith001" 
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				class="author-name" itemprop="name"				>prith001</span>
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		<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 🤗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>

		
		
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