Sentence Level Semantic Classification of Online Product Reviews of Mixed Opinions Using Naive bayes Classifier

Recent years have marked the beginning and rapid expansion of the social web, where people can freely express their opinion on different objects such as products, persons, topics etc on blogs, forums or e-commerce sites and opinion analysis is one emerging research field. As e-commerce is fast growing, product reviews on the Web have become an important information source for customers’ decision making when they plan to buy products online. Classifying the reviews automatically into different semantic orientations has become a major problem for customers as the reviews are too many for the customers to go through. In this paper we propose a different approach which performs the sentence level classification even the reviews contains mixed opinions. In this approach, a typical feature selection method based on sentence tagging is employed and a naive bayes classifier is used to create a base classification model, which is then combined with certain heuristic rules for review sentence classification. Experiments show that this approach achieves better results than using general naive bayes classifiers. Keywords— Sentence level classification, naive bayes classifier, sentence tagging