Using Stylometric Features for Sentiment Classification

This paper is a comparative study about text feature extraction methods in statistical learning of sentiment classification. Feature extraction is one of the most important steps in classification systems. We use stylometry to compare with TF-IDF and Delta TF-IDF baseline methods in sentiment classification. Stylometry is a research area of Linguistics that uses statistical techniques to analyze literary style. In order to assess the viability of the stylometry, we create a corpus of product reviews from the most traditional online service in Portuguese, namely, Buscapé. We gathered 2000 review about Smartphones. We use three classifiers, Support Vector Machine (SVM), Naive Bayes, and J48 to evaluate whether the stylometry has higher accuracy than the TF-IDF and Delta TF-IDF methods in sentiment classification. We found the better result with the SVM classifier (82,75%) of accuracy with stylometry and (72,62%) with Delta TF-IDF and (56,25%) with TF-IDF. The results show that stylometry is quite feasible method for sentiment classification, outperforming the accuracy of the baseline methods. We may emphasize that approach used has promising results.