Sentiment Analysis of Twitter Data Using Machine Learning Techniques

Abstract

Online Microblogging on social networks have been used for indicating opinions about certain entity in very short messages. Existing some popular microblogs like twitter, facebook etc,in which twitter attains maximum amount of attention in the field of research areas related to product, movie reviews, stock exchange etc. The research on sentiment analysis has been going for a long time. Sentiment analysis in present days becomes the major issue in field of research and technology. Due to day by day increase in the number of users on the social networking websites, huge amount of data produces in the form of text, audio, video and images. There is need to do sentiment analysis as texts in form of messages or posts to find the whether the sentiment is negative, positive or neutral. We had extracted data from twitter i.e. movie reviews for sentiment prediction using machine-learning algorithms. We applied supervised machine-learning algorithms like support vector machines (SVM), maximum entropy and Naïve Bayes to classify data using unigram, bigram and hybrid i.e. unigram + bigram features. Result shows that SVM surpassed other classifiers with remarkable accuracy of 84% for movie reviews.