Identifying and Ranking Dominating Product Features using NLP Technique

Abstract

As internet usage increased users uses internet not only to access and search information but also at the same time able to spread and publish own idea, sentiments, knowledge via different number of websites. Different websites encourage their user to write their views in the form of electronic text. This system increasing user-written electronic text in the world of internet, large numbers of user opinions are available on World Wide Web. User review contains important information, which is beneficial for customer as well as retailer. These reviews are in scattered format so extracting important data from this large corpus is time consuming work. Here developing a system which will automatically identify and rank the product features. The Stanford parser is used for identify product features. Sentence level sentiment classification is used for identify sentiment of each sentence separately, Sentiment Classifier is used for classifying each sentence, and finally a probabilistic ranking algorithm is used to rank the product features. General Terms Sentiment classification, Sentence level sentiment classification, Natural Language Processing, probabilistic aspect ranking