Abstract: “With the rapid growth of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. Sentiment analysis extracts, identifies and measures the sentiment or opinion of documents as well as the topics within these documents. The Naïve Bayes algorithm performs a boolean classification i.e. it classifies a document as either positive or negative according to its sentiment. We have already seen by Sayeedunnisa et al [1], that the application of Naïve Bayes trained on high value features, extracted from a bag-of-words model, yields an accuracy of 89.2%. This paper studies the application of Naïve Bayes technique for sentiment analysis by including training of bigram features to improve accuracy and the overall performance of the classifier. We also evaluate the impact of selecting low vs. high value features, calculated”

Keywords: Naïve Bayes, Information Gain, Sentiment Analysis, Social Network, Twitter, Cloud Computing