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Sentiment Analysis (SA) is one of the greatest broadly planned applications of Natural Language Processing (NLP) and Machine Learning (ML). This field has grown enormously with the advent of the Web 2.0. The Internet has as long as a platform for people to express their opinions, emotions and feelings towards products, persons, and life in general. Accordingly, the Internet is nowadays a massive resource of opinion amusing written data. A vital job of sentiment analysis is sentiment classification, which intentions to automatically classify opinionated text as being negative, positive, or neutral. This paper provides a comparative study on sentimental analysis and its applications mostly for recommendation system. Recommender systems have grown to be a serious research area after the emergence of the first paper on collaborative filtering in the Nineties.

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