EXTRACTING AND ANALYZING SENTIMENTS OF THE CROWD USING NAÏVE BAYES CLASSIFICATION

Abstract

In this paper we design and develop restaurant review summarization using Naïve bayes Classification with tf-idf. The restaurant review information is based on the sentiment classification result. The condensed description of restaurant reviews are generated from the feature based summarization. We propose a novel approach based on TF-IDF to identify the product features. By using Bayes classifier the main texts in the review are classified into two categories at the sentence level: positive review and Negative review. We consider sentiment classification accuracy to evaluate the system. The rating and review summarization system can be extended to other product review domains easily.