During the decision-making process, everybody wants to hear feedback or review about the product. But, manually going through each review is very time-consuming and difficult to get a proper conclusion regarding the product which implies the need for sentiment analysis. Sentiment analysis is an automatic method used to determine whether the review about a subject is positive or negative. In sentiment analysis, for automatic review mining, there are lots of words whose different senses depend on the context they are used. This is the issue in sentiment analysis which is called word sense disambiguation. Most of the existing sentiment analysis techniques determine the polarity without any word sense disambiguation. Few methods have been proposed to achieve this as they are not able to properly disambiguate the context in which the words are used. Here is discussed a feature level sentiment analysis method, which produces a summary of opinions about different product features. A word sense disambiguation method is deployed which accurately locates the sense of a word in a sentence while determining the polarity.
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