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A Survey on Unsupervised Joint Topic Modeling Approach in Bayesian Model
Abstract
Social media is one of the biggest forums to express opinions. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisal and emotions of people in regards to entities, events and their attributes. Sentiment analysis is also known as Opinion Topic Modeling (OTM) approach. OTM is to analyze and cluster the user generated data like reviews, blogs, comments, articles etc. These data find its way on social networking sites like twitter, facebook etc. Twitter has provided a very massive space for prediction of consumer brands, movie reviews, democratic electoral events, stock market, and popularity of celebrities. This survey paper discuss several methods used for sentiment analysis. This paper mainly focused on Bayesian Naive Bayes, a Bayesian model for unsupervised sentiment topic modeling classification. It is showed that BNB is superior to the LDA model on the standard unsupervised sentiment classification task.
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