Twitter Sentiment Analysis Using Naive Bayes-Based Machine Learning Technique

Abstract

“Computational” sentiment analysis can determine whether a sentiment is favorable, negative, or neutral. Another term for this approach is “opinion mining,” or obtaining a speaker’s sentiments. Businesses use it to develop strategies, learn what customers think about products or brands, how people react to campaigns or new product releases, and why they do not buy certain products. It is used in politics to keep track of political ideas and to check for contradictions between government claims and actions. It can even be used to predict election results! It is also used to track and analyze social phenomena like recognizing dangerous circumstances and evaluating blogging mood. In this paper, we look tackle the problem of sentiment categorization using the Twitter dataset. To analyze sentiment, preprocessing and Naive Bayes classifier approaches are utilized. As a result, we applied a text preprocessing classification accuracy classifying strategy and improved our classification accuracy score on the Kaggle public leaderboard. The aim of this paper is to classify the twitter sentiments using machine learning algorithm based on Naïve Bayes Classifier. The proposed model indicated better accuracy and precision based on performance parameters such as precision, recall and accuracy.