Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment (or polarity). Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well-being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative (non-negated) contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term (a word or a phrase) within a message (term-level task) using R language.
opinion mining
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