Comparative analysis of Twitter data using supervised classifiers

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. 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 Naive 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.