Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment

A new framework is designed for sentiment classification and feature based summarization system in a mobile environment. Posting online reviews has become an increasingly popular way for people to share their opinions about specific product or service with other users. It has become a common practice for web technologies to provide the venues and facilities for people to publish their reviews. Sentiment classification and feature based summarization are essential steps for the classification and summarization of movie reviews. System proposed Random forest method for sentiment classification of movie reviews. Identification of movie features and opinion words are both important for feature based summarization. System identified movie features using a novel approach called Latent Semantic Analysis (LSA) and frequency based approach. Then system identified opinion words using part-of-speech (POS) tagging method. The result of LSA is extended to LSA based filtering mechanism to reduce the size of review summary. System design focused on the sentiment classification accuracy and system response time.