Identifying the best feature combination for sentiment analysis of customer reviews

Opinions are increasingly available in form of reviews and feedback at websites, blogs, and microblogs which influence future customers. From human perspective, it is difficult to read all the opinions and summarize them which require an automated and faster opinion mining to classify the reviews. In this paper different features namely, N-gram features, POS based features and features based on the lexicon SentiWordNet, have been investigated. The Support Vector Machines (SVM) classifier has been modeled with presence as feature representation for classification of the reviews into positive and negative classes thereby identifying the best feature combination. Results of Experiments conducted on smart phone reviews for different feature combinations have been presented. A highest accuracy up till 92% and 95% has been obtained for small and large datasets, respectively.