Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches

Abstract

This paper presents our experimental results on performance evaluation of all the three approaches for document-level sentiment classification. We have implemented two Machine Learning based classifiers (Naïve Bayes and SVM), the Unsupervised Semantic Orientation approach (SO-PMI-IR algorithm) and the SentiWordNet approaches for sentiment classification of movie reviews. We used two pre-existing large datasets and collected one of moderate size on our own. The paper primarily makes two useful contributions: (a) it presents a comprehensive evaluative account of performance of all the three available approaches on use with movie reviews, and (b) it presents a new modified Adjective+Adverb combine scheme of SentiWordNet approach.