Simple approaches of sentiment analysis via ensemble learning

Abstract

Twitter has become a popular microblogging tool where users are increasing every minute. It allows its users to post messages of up to 140 characters each time; known as ‘Tweets’. Tweets have become extremely attractive to the marketing sector, since the user can either indicate customer success or presage public relations disasters far more quickly than web pages or traditional media. Moreover, the content of Tweets has become a current active research topic on sentiment polarity as positive or negative. Our experiment of sentiment analysis of contexts of tweets show that the accuracy performance can improve and be better achieved using ensemble learning, which is formed by the majority voting of the Support Vector Machine, Naive Bayes, SentiStrength and Stacking.