SENTIMENT ANALYSIS USING A NOVEL APPROACH TO CLASSIFY SENTIMENTS IN SOCIAL NETWORKING DATA
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Abstract
Sentiment analysis is the task of finding polarity in the given document. The document could be a sentence, a paragraph or a document with number of pages. Polarity of the document could be positive, negative or neutral. This polarity reflects the mood and emotions of the user. Twitter is the most popular social media today. It is the biggest platform for communication. In this research, tweets from twitter is taken for sentiment analysis. The biggest challenge lies in identifying the document accurately for its polarity. There are number of machine learning algorithms available using supervised or semi supervised technique. These algorithms apply unigram, bigram, n-gram or hybrid approach. Semi supervised learning is being used for this research paper. In this work, unigram and bigram approach are combined together to form novel model that uses Naïve Bayes approach and results were found. This novel approach gave a better result. A time based analysis was also performed in order to find the day wise polarity of the tweets
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