In past few years prevalence of internet usage is increasing. Different online shopping sites have many options of purchasing the product while shopping. Users share experiences in the form of reviews. The number of reviews shared by people are increasing. So it is difficult to find the right information about the product. Traditional recommender systems (RS) makes use of different factors, such as users purchase records, geographical location etc. We propose sentiment based recommender system. Based on the sentiment word in the CPRST system, the review has been rated by finding sentiment score. Also textual reviews are categorized into different feature of product using text classification technique. Experimental results of CPRST system show that user preference can be characterized by the sentiment from text review and it can improve the performance of recommendation system. Using LDA method we classified text review and this resulted in good results in nickel.

Item reputation, Text Reviews, Rating prediction, Recommender system, Sentiment influence, User sentiment, Sentiment analysis, Text classification

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