A hybrid approach to sentiment analysis with benchmarking results

Abstract

The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Naïve Bayes and Maximum Entropy. Using the well known and publicly available Movie Review Dataset, the proposed hybrid system achieved higher accuracy and precision than Naïve Bayes (NB) and Maximum Entropy (ME).