Fast and accurate - Improving lexicon-based sentiment classification with an ensemble methods

Abstract

A lexicon-based ensemble approach to sentiment analysis that outperforms lexicon-based method is presented in this article. This method consists of two steps. First we employ our own method (called frequentiment) for automatic generation of sentiment lexicons and some of publicly available lexicons. Secondly, an ensemble classification is used to improve the overall accuracy of predictions. Our approach outperforms publicly available sentiment lexicons and automatically generated domain lexicons. We conduct comprehensive analysis based on 10 Amazon review data sets that consist of 4,200,000 reviews.