Sentiment Classification into Three Classes Applying Multinomial Bayes Algorithm, N-grams, and Thesaurus

Abstract

The paper is devoted to development of the method that classi?es texts in English and Russian by sentiments into positive, negative, and neutral. The proposed method is based on the Multinomial Naive Bayes classi?er with additional n-grams application. The classi?er is trained either on three classes, or on two contrasting classes with a threshold to separate neutral texts. Experiments with texts on various topics showed signi?cant improvement of classification quality for reviews from a particular domain. Besides, the analysis of thesaurus relationships application to sentiment classification into three classes was done, however it did not show significant improvement of the classification results.

Keywords

• sentiment classification
• text classification
• classification into three classes
• n-grams
• thesaurus
• machine learning