Sentiment Analysis Naive Bayes Github

It provides a consistent API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and more. Survey on Aspect-level sentiment analysis, Schouten and Frasnicar, IEEE, 2016 ! Twitter mood predicts the stock market, Bollen, Mao, and Zeng, 2010 ! Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts, Cicero Nogueira dos Santos & Maira Gatti, 2014 L Sanders 30 References and Reading. What is Sentiment Analysis? Sentiment analysis is more than just a feature in a social analytics tool - it's a field of study. I am doing sentiment analysis on tweets. tech student, Department of CSE DCRUST,Murthal Neetu verma Assistant Professor, Department of CSE DCRUST,Murthal Abstract—In recent years, the remarkableexpansion of web technologies, lead to an massive quantity of user. Skip to content. In Advances in Information Retrieval, 337-349. In this process, at first the positive and negative features are combined and then it is randomly shuffled. sentiment analysis with twitter 03: building models to predict for twitter data from nltk ## naive bayesian clf_nb pytorch, base, sentiment analysis, sql. Naive Bayes is a high-bias, low-variance classifier, and it can build a good model even with a small data set. documents, web blogs/articles and general phrase level sentiment analysis. Sentiment analysis is a complicated problem but experiments have been done using Naive Bayes, maximum entropy classifiers and support vector machines. A comprehensive analysis of this dataset is presented and two popular classifiers, support vector machine (SVM) and Naive Bayes (NB) are used for empirical experimentations. Sentiment Analysis using Naïve Bayes Classifier Pooja jain M. We employ a 2-layer Bidirectional LSTM, equipped with an attention mechanism (Rocktäschel et al. documents, web blogs/articles and general phrase level sentiment analysis. For sentiment analysis, a Naive Bayes classifier is one of the easiest and most effective ways to hit the ground running for sentiment analysis. It is simple to use and computationally inexpensive. Probability can be related to our regular life and it helps us to solve a lot of real-life issues. Naive Bayes or Naive Bayes Classifier has its foundation pillar from the concept of Bayes theorem explained by the theory of probability. Thus, I think it's safe to assume that, if the Naive Bayes algorithm outputs exactly -1. Because of large size of emerging data, the sentiment analysis of review will come under big data. The Naive Bayes Classifier is a well-known machine learning classifier with applications in Natural Language Processing (NLP) and other areas. Evaluation/Setup Baseline For my baseline, I test my classifiers against a hand labelled test set of 177 negative and 182 positive tweets. In abhy/sentiment: Tools for Sentiment Analysis. Sentiment Analysis is mainly used to gauge the views of public regarding any action, event, person, policy or product. In my last post, I introduced sentiment analysis, the Naïve Bayes classification technique and why you or your business might be interested in this. An Intuitive Guide to Financial Analysis with Data Transformations Naive Bayes: Intuition and Implementation \u2013 Towards Data Science GitHub - mKausthub. A clear majority of people share their opinions about various aspects of their lives online every day. AIM OF THE PROJECT The purpose of this project is to build an algorithm that can accurately classify Twitter messages as positive or negative, with respect to a query term. 3-HBP: A Three-Level Hidden Bayesian Link Prediction Model, Network computing projects, IEEE 2018 projects at project station. After estimating the parameters of the naive Bayes model using training data, test data was classified between. Both approaches analyse a corpora of positive and negative Movie Review data by training and thereafter testing to get an accuracy score. GitHub Gist: instantly share code, notes, and snippets. Making it easier for you and other investors -. Creating a Text Classifier with Naive Bayes. Typical cases are blog posts, where the author expresses an opinion about a product, among many other things, or large product comparison articles, where the product that we are interested in is. Chi has 3 jobs listed on their profile. Naive bayes notes. Output is 0. As Twitter gains popularity, it becomes more useful to analyze trends and sentiment of its users towards various topics. Before we take a look at the code, let’s go through a brief introduction of Naive Bayes classification and see how we can use it to identify tweet sentiment. Vinodhini, M. How to build your own Facebook Sentiment Analysis Tool. Bayesian Theorem and its connection to Sentiment Analysis - Jamie Maguire. (4) Build a multilayer Naïve Bayes model on account of Naïve Bayes models from different dimensions to complete user’s retweeting sentiment tendency analysis in a more fine-grained perspective. It marks a sentence as positive, negative or neutral depending on the kind of words that are used, this can help in automatically selecting a review, comment or chat that has the best. granular taxonomies, sentiment analysis, document summarization, and entity relation modeling. Classifiers: Multinomial Naive Bayesian (maximum likelihood approach) and Stochastic Gradient Descent. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. In this article, we describe the learning strate-gies we developed so as to perform this task, all of them based on bayesian classication. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy classifier by using unigrams, bigrams and unigram and bigrams together. Approaches vary from using a l. Twitter live Sentiment Analysis helps us map the positive and the negative sentiments of tweets in real time. Their approach is to use a list of positive and neg-ative keywords. In my last post, I introduced sentiment analysis, the Naïve Bayes classification technique and why you or your business might be interested in this. Apply Dimensionality Reduction technique using Principal Component Analysis (PCA) on customer dataset except on dependent variable and reduce it to two dimensions. Naive bayes classifier. With growth of people that go on the Internet to express their opinions about brands and other matters, Sentiment Analysis tools are becoming increasingly important to help companies and individuals to react in a timely manner to incidents that affect their business and reputation. Machine learning makes sentiment analysis more convenient. Indeed Naive Bayes is usually outperformed by other classifiers, but not always! Make sure you test it before you exclude it from your research. This post discusses lexicon-based sentiment classifiers, its advantages and limitations, including an implementation, the Sentlex. One common use of sentiment analysis is to figure out if a text expresses negative or positive feelings. Millions of messages are appearing daily in popular web-sites that provide services for microblogging such as Twitter, Tumblr, Facebook. So now we use everything we have learnt to build a Sentiment Analysis app. In the previous part, we have learned the theory behind the genius of Naive Bayes Classifier in Sentiment Analysis. In this paper, we detect online hotspot forums by computing sentiment analysis for text data available in each forum. As you can see, references to the United Airlines brand grew exponentially since April 10 th and the emotions of the tweets greatly skewed towards negative. The next step is to train a classifier on the training data. SENTIMENT ANALYSIS USING NAÏVE BAYES CLASSIFIER CREATED BY:- DEV KUMAR , ANKUR TYAGI , SAURABH TYAGI (Indian institute of information technology Allahabad ) 10/2/2014 [Project Name] 1 2. In the previous part, we have learned the theory behind the genius of Naive Bayes Classifier in Sentiment Analysis. Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis (ST, XC, YW, HX), pp. can be used get the source from github and run it , Luke!. Honestly, I would recommend you try developing this mostly on your owns as it would seem as this is some kind of introductory analysis, but you may find tools lik. A live screencast of an exploratory data analysis from the Tidy Tuesday. In this blog I will discuss the theory behind three popular Classifiers (Naive Bayes, Maximum Entropy and Support Vector Machines) in the context of Sentiment Analysis. In their sur-vey of the eld, Pang and Lee (2008) highlight the importance of sentiment analysis across a range of industries, including review aggregation web-sites, business intelligence, and reputation man. sentiment analysis of movie reviews using NLTK naive bayes - sentiment_analysis. Text classification aims to assign documents (emails, tweets. Garcia, “ Citius: A naïve-bayes strategy for sentiment analysis on english tweets,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (Association for Computational Linguistics and Dublin City University, Dublin, Ireland, 2014), pp. Li, Zhaoyu. We will talk again about sentiment analysis, this time we will solve the problem using a different approach. Purpose of sentiment analysis Why Twitter Data Challenges of Using Twitter Data Introduction 3. Sentiment Analysis using Naïve Bayes Classifier Pooja jain M. Their approach is to use a list of positive and neg-ative keywords. Machine learning makes sentiment analysis more convenient. Then we can say that Naive Bayes algorithm is fit to perform sentiment analysis. This paper outlines a new language-independent model for sentiment analysis of short, social-network statuses. Text-based sentiment analysis offers valuable in-sight into the opinions of large communities of re-viewers, commenters and customers. Mend`es-France 69676 Bron Cedex, France. Microblogging today has become a very popular communication tool among Internet users. The latter uses LASSO regularization as a statistical approach to select. As a baseline, we use Twittratr’s list of keywords, which is publicly available2. In this study, we used brightfield PLA, combined with a systematic sampling and a parameter-free naive Bayesian classifier, and demonstrated proximity between D2R and A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. Now is the time to see the real action. Sentiment analysis is the use of statistics, natural language processing and machine learning to extract or categorize the sentiment content of a piece of sample text. Let’s parse that. Generally, this type of sentiment analysis is useful for consumers who are trying to research a product or service, or marketers researching public opinion of their company. Skip to content. Jupyter We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). Multinomial Naive Bayes allows us to represent the features of the model as frequencies of their occurrences (how often some word is present in our review). As you've already been shown, we can actually save tons of time by pickling, or serializing, the trained classifiers, which. A Project Report on SENTIMENT ANALYSIS OF MOBILE REVIEWS USING SUPERVISED LEARNING METHODS A Dissertation submitted in partial fulfillment of the requirements for the award of the degree of BACHELOR OF TECHNOLOGY IN COMPUTER SCIENCE AND ENGINEERING BY Y NIKHIL (11026A0524) P SNEHA (11026A0542) S PRITHVI RAJ (11026A0529) I AJAY RAM (11026A0535) E RAJIV (11026A0555. 1 Baseline Twittratr is a website that performs sentiment analysis on tweets. We show in this thesis that using classifier ensembles such as majority voting classifier formed by Decision Tree, Bernoulli Naive Bayes,. Following is the screenshot of program. This package is an implementation of the Naive Bayes Algorithm To Determine the sentiment of a particular statement, a book review, chat, speech and so on. Semantic Analysis. Naive Bayes SVM for Sentiment Analysis. Within a single pass to the training data, it computes the conditional probability distribution of each feature given label, and then it. 11_Naive_Bayes (NB HTML) 18_Discriminant_Analysis (NB HTML) JSON | Using the NLTK package to conduct sentiment analysis without a dictionary. Here I'm going to explain at a surface level how this analysis actually works, the implications of automating sentiment analysis, and what the future holds for sentiment analysis. For our research, we are going to use the IRIS dataset, which comes with the Sckit-learn library. The Naive Bayes classifier aggregates information using conditional probability with an assumption of independence among features. - abdulfatir/twitter-sentiment-analysis. Pang et al. Rather, it means the sentence contained no training-set words, and the library authors arbitrarily chose an arbitrary output in that case. GitHub issue tracker. In this study, we used brightfield PLA, combined with a systematic sampling and a parameter-free naive Bayesian classifier, and demonstrated proximity between D2R and A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. The data was amazon reviews of various products and the goal was to classify the reviews as either positive or negative. How close am i to the reality ? I am neither a data scientist nor a statistician, but this is a summary of what i THINK happens in Naive Bayes algorithms for Sentiment Analysis, in Scikit Learn. The classifiers compared are support vector machines and Naive Bayes over the same data set. zip file Download. For ex-ample, Xu Yun[8] el al from Stanford University applied existing supervised learning algorithms such as perceptron algorithm, naive bayes and supporting vector machine to predict a review’s rating on Yelp’s rating dataset. sentiment analysis of movie reviews using NLTK naive bayes - sentiment_analysis. jKool Sample Repository Demonstrating Bayes. The class with the highest posterior probability is the outcome of prediction. Approaches vary from using a l. In this post I pointed out a couple of first-pass issues with setting up a sentiment analysis to gauge public opinion of NOAA Fisheries as a federal agency. Li, Zhaoyu. airline travelers. In SentimentAnalysis: Dictionary-Based Sentiment Analysis. Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning @article{Troussas2013SentimentAO, title={Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning}, author={Christos Troussas and Maria Virvou and Kurt Junshean Espinosa and Kevin Llaguno and Jaime Caro}, journal={IISA 2013}, year={2013}, pages={1-6} }. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. 6 million random tweets with corresponding binary labels. Sentiment Analysis is automatic extraction of subjective content of text and predicting the subjectivity such as positive or negative. Description Usage Format Note Source Examples. Annotate a batch of Tweets with the sentiment analysis information. October 16, 2018. Now it is time to choose an algorithm, separate our data into training and testing sets, and press go! The algorithm that we're going to use first is the Naive Bayes classifier. How to build your own Facebook Sentiment Analysis Tool. Can we do sentiment analysis of movie reviews to determine if the reviews are positive or negative? Contents. Even if we are working on a data set with millions of records with some attributes, it is suggested to try Naive Bayes approach. Sentiment-Analysis-Twitter-Ayush Pareek. It marks a sentence as positive, negative or neutral depending on the kind of words that are used, this can help in automatically selecting a review, comment or chat that has the best. Harsh Vrajesh Thakkar, bearing Roll No: P11CO010 and submitted to the Computer Engineering Department at. Sentiment analysis is the use of statistics, natural language processing and machine learning to extract or categorize the sentiment content of a piece of sample text. Such as Natural Language Processing. Twitter live Sentiment Analysis helps us map the positive and the negative sentiments of tweets in real time. For our purposes the provided parser is used to extract the sentences of the text and then the sentiment analysis tool scores them one by one. Naive Bayes (NB), Maximum entropy (ME) and Support. Naive Bayes works on Bayes Theorem of probability to predict the class of a given data point. Figure 1: Sentiment Analysis Model [20] 2. Govindarajan Abstract The area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc. Social Media Monitoring is one of the hottest topics nowadays. In their sur-vey of the eld, Pang and Lee (2008) highlight the importance of sentiment analysis across a range of industries, including review aggregation web-sites, business intelligence, and reputation man. Sebastian Wagner,BSc, “Ageing-based Multinomial Naive Bayes Classifiers over Data Streams”, LMU Munich, 2015. Einstein 34000 Montpellier, France mde@amisw. In the next blog I will apply this gained knowledge to automatically deduce the sentiment of collected Amazon. cn Yang Xiang Tongji University Shanghai China shxiangyang@tongji. There are three main classification types, namely feature level, sentence level, and. For deeper explanation of MNB kindly use this. Micro-blogging Sentiment Analysis Using Bayesian Classification Methods Suhaas Prasad I. Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc. Sign in Sign up Instantly share code. My goal of this post is to show how to implement. Despite its simplicity, it is able to achieve above average performance in different tasks like sentiment analysis. Making it easier for you and other investors -. Description. com) & Yan Yang(yxy128@berkeley. Bayes - English mathematician for whom Bayes' theorem is named Thomas Bayes. In recent years, we have seen the democratization of sentiment analysis, in that it’s now being offered as-a-service. You can check out the. Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning Christos Troussas, Maria Virvou Department of Informatics. This package is an implementation of the Naive Bayes Algorithm To Determine the sentiment of a particular statement, a book review, chat, speech and so on. Survey on Aspect-level sentiment analysis, Schouten and Frasnicar, IEEE, 2016 ! Twitter mood predicts the stock market, Bollen, Mao, and Zeng, 2010 ! Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts, Cicero Nogueira dos Santos & Maira Gatti, 2014 L Sanders 30 References and Reading. Indeed Naive Bayes is usually outperformed by other classifiers, but not always! Make sure you test it before you exclude it from your research. You just need to sign up for free to MonkeyLean, click on create a model, and choose Classifier: Then, choose the type of classification task you would like. Index Terms —Sentiment Analysis, Naïve Bayes', K-NN, Supervised Machine Learning, Text Mining. Photo by Jerry Kiesewetter on Unsplash. In this study, we used brightfield PLA, combined with a systematic sampling and a parameter-free naive Bayesian classifier, and demonstrated proximity between D2R and A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. as Support Vector Machines, Neural Networks, Naive Bayes Classifier, Decision Trees, Rocchio's Algorithms, and k-nearest neighbor [16]. BENEFITS OF SYSTEM The benefits of stock market could include: 1. Wandeep Kaur, and Vimala Balakrishnan. Output is 0. The top 40 generalizable features between stock reviews and computer reviews - "Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis". Looking for more notebook examples? Visit the Data Science Experience Learning Center. edu) Modeling Method The purpose of the homework is to construct a valid Naïve Bayes predictor for sentiment analysis of several documents - to predict whether a given document indicates a favorable opinion of the written. We have divided our data into training and testing set. It is ideal to use Naïve Bayes as benchmark, given its wide use, proven 83 robustness and satisfactory result. Text classification aims to assign documents (emails, tweets. FRAMEWORK: Python’s NLTK toolkit and its sentiment analyzer module. According to the author, these estimates are based on probabilistic methods, or methods concerned with describing uncertainty. Schneider East Tennessee State University Follow this and additional works at:https://dc. In this blog I will discuss the theory behind three popular Classifiers (Naive Bayes, Maximum Entropy and Support Vector Machines) in the context of Sentiment Analysis. Both LSTM and GF-RNN weren’t written specifically focusing on sentiment analysis, but a lot of sentiment analysis models are based on these two highly cited papers. Sentiment analysis digunakan untuk membangun sebuah sistem melalui pengumpulan dan analisis komentar terhadap produk yang dibuat dari review atau tweet, comments, dan blog spot dalam sebuah web. The Naive Bayes Classifier is a well-known machine learning classifier with applications in Natural Language Processing (NLP) and other areas. Machine learning makes sentiment analysis more convenient. The data was amazon reviews of various products and the goal was to classify the reviews as either positive or negative. This is definitely correct. Sentiment analysis experiment using scikit-learn ===== The script sentiment. In this paper we present a supervised sentiment classification model based on the Naïve Bayes algorithm. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Max-. anger, disgust, fear, joy, sadness, surprise) of a set of texts using a naive Bayes classifier trained on Carlo Strapparava and Alessandro Valitutti's emotions lexicon. For deeper explanation of MNB kindly use this. Their approach is to use a list of positive and neg-ative keywords. tech student, Department of CSE DCRUST,Murthal Neetu verma Assistant Professor, Department of CSE DCRUST,Murthal Abstract—In recent years, the remarkableexpansion of web technologies, lead to an massive quantity of user. 4-02 Simple Text Mining For Sentiment Analysis Of Political Figure Using NaÕve Bayes Classifier Method SIMPLE TEXT MINING FOR SENTIMENT ANALYSIS OF POLITICAL FIGURE USING NAÏVE BAYES CLASSIFIER METHOD Yustinus Eko Soelistio *, Martinus Raditia Sigit Surendra † System Information, Faculty of Information and Communication Technology, Multimedia Nusantara University Jl. Sentiment analysis experiment using scikit-learn ===== The script sentiment. What is Naive Bayes? Naive Bayes is the application of Bayes theorem using naive assumptions… What the hell does that mean?. This project explores a few optimizations to the typical Naive Bayesian methods for sentiment analysis along with a few notes. I have not tried that exactly, but wanted. positive, negative, neutral. Sentiment analysis teaches computers to recognise the human emotions present in text. Zürich Machine Learning and Data Science Meetup 12,318 views. sentiment analysis of movie reviews using NLTK naive bayes - sentiment_analysis. According to the author, these estimates are based on probabilistic methods, or methods concerned with describing uncertainty. One of the major challenges is understanding the. gz file is maintained by imjalpreet. Naive Bayes in sentiment analysis: Machine Learning Questions & Answers Part - IV Neel Artificial Intelligence , Azure Machine Learning , Machine Learning , Machine Learning in simple words , Machine Learning Interview Questions , ML , Naive Bayes December 8, 2017 3 Minutes. In this paper, we detect online hotspot forums by computing sentiment analysis for text data available in each forum. We show in this thesis that using classifier ensembles such as majority voting classifier formed by Decision Tree, Bernoulli Naive Bayes,. Classification accuracy is measured in terms of general Accuracy, Precision, Recall, and F-measure. documents, web blogs/articles and general phrase level sentiment analysis. pada pengertian lain Sentimen analisis adalah riset komputasional dari opini sentiment dan emosi yang diekspresikan secara tekstual. A presentation created with Slides. Moreover when the training time is a crucial factor, Naive Bayes comes handy since it can be trained very quickly. , battery, screen ; food, service). Dhiren Patel 2 1M. Review of Sentiment Analysis using Naive Bayes and Neural Network Classifier International Journal of Scientific Engineering and Technology Research Volume. that make analysis more difficult, Opinion Mining and Sentiment Analysis Techniques are means of analyzing the above information. Toran Verma2 1RCET, Bhilai Dept. Here we will use 5 classes to distinguish between very negative sentence (0) and very positive. There are four types of classes are available to build Naive Bayes model using scikit learn library. Sentiment analysis teaches computers to recognise the human emotions present in text. A Naïve Bayes Classifier for Sentiment Siamak Faridani (faridani@gmail. It marks a sentence as positive, negative or neutral depending on the kind of words that are used, this can help in automatically selecting a review, comment or chat that has the best. Read stories about Naive Bayes on Medium. I've found a similar project here: Sentiment analysis for Twitter in Python. can be used get the source from github and run it , Luke!. •Sentiment analysis Bayes rule + Conditional independence assumption. In short, it is a probabilistic classifier. Multinomial Naive Bayes: This Naive Bayes model used for document. Naive Bayes, Support Vector Machines, etc. Just my blog Cyril Vandenberghe cyril. Naive Bayes and Sentiment Analysis A few years back one of my friends and I built a forum where developers could post useful tips regarding the technology they were using. Sentiment analysis is one of the most common applications in natural language processing. It provides a consistent API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and more. Sentiment Analysis: Naive Bayes Classifier from scratch in Golang - Naivebayes. Naive bayes classifier. Such as Natural Language Processing. For our purposes the provided parser is used to extract the sentences of the text and then the sentiment analysis tool scores them one by one. Sentiment Analysis using Python: We are using Python for sentiment analysis to show the power of python in just few lines of code. I was looking for a way to classify short texts into several categories. Creating a Text Classifier with Naive Bayes. Essentially what that block of code does is splits up the reviews by line and then builds a posFeatures variable which contains the output of our feature selection mechanism (we'll see how that works in a minute) with 'pos' or 'neg' appended to it, depending on. Naive bayes notes. Type of attitude From a set of types Like, love, hate, value, desire, etc. As Twitter gains popularity, it becomes more useful to analyze trends and sentiment of its users towards various topics. KDD-2009-MelvilleGL #analysis #classification Sentiment analysis of blogs by combining lexical knowledge with text classification ( PM , WG , RDL ), pp. This assumption is called class conditional independence. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e. Contribute to Yangruipis/NaiveBayesSentimentAnalysis development by creating an account on GitHub. Also, share this article so that it can reach out to the readers who can actually gain from this. Naive Bayes are a family of powerful and easy-to-train classifiers that determine the probability of an outcome given a set of conditions using Bayes' theorem. py reproduces the sentiment analysis approach from Pang, Lee and Vaithyanathan (2002), who tried to classify movie reviews as positive: or negative, with three differences: * tf-idf weighting is applied to terms * the three-fold cross validation split is different. Keywords: Naïve Bayes classifier, Holdout method, K-fold cross validation, Leave-one-out cross validation I. RNTN was introduced in 2011-2012 by Richard Socher et al. Creating a Text Classifier with Naive Bayes. 1) Naive Bayes: Naive Bayes model is a simplest model. Twitter Sentiment Analysis using Naive Bayes. Let's have a brief look at maths. This approach can be important because it allows you to gain an understanding of the attitudes, opinions, and emotions of the people in your data.