Paper Title
A Comprehensive Approach To Analyse And Rank Products Using Sentiment And Feature

The World Wide Web can be viewed as a repository of opinions from users spread across various websites and networks and today’s netizens look up reviews and opinions to judge commodities, visit forums to debate about events and policies. With this explosion in the volume of and reliance on user reviews and opinions, manufacturers and retailers face the challenge of automating the analysis of such big amounts of data (user reviews, opinions, sentiments). Armed with these results, sellers can enhance their product and tailor experience for the customer. Similarly, policy makers can analyze these posts to get instant and comprehensive feedback. Or use it for new ideas that democratize the policy making process. This paper is the outcome of our research in gathering opinion and review data from popular portals, e-commerce websites, forums or social networks; and processing the data using the rules of natural language and grammar to find out what exactly was being talked about in the user's review and the sentiments that people are expressing. Our approach diligently scans every line of data, and generates a cogent summary of every review (categorized by aspects) along with various graphical visualizations. A novel application of this approach is helping out product manufacturers or the government in gauging response. Keywords— Sentiment analysis; feature extraction matrix; reviews; SAFE (sentiment analysis and feature extraction) miner.