Sentiment mining from Bangla data using mutual information

Due to the explosion of social networking sites, blogs and review sites (for example, Amazon, Twitter, and Facebook, etc.) it provides an overwhelming amount of textual information. We need to organize, explore, analyze the information for making a better decision from the side of customers and companies. Thus, sentiment analysis is the best way in which it determines the author's feelings expressed in reviews as positive or negative opinions by analyzing an enormous number of documents. In this work, we used Mutual Information (MI) for the feature selection process and also used Multinomial Naive Bayes (MNB) for the classification of Bangla and English reviews. The experimental results demonstrate that the system can achieve satisfactory accuracy for Bangla dataset compare to English dataset where Bangla dataset is generated from Amazon's Watches English dataset.