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Sentiment Analysis On Twitter Using Dynamic Fuzzy Approach

S. Indhu, S. R. Lavanya


Social media is one of the most important forums to convey opinions. Sentiment analysis is a sequence of methods for identifying and extracting information from user-created data like reviews, blogs, comments, articles etc. Usually, sentiment analysis has been about opinion polarity, i.e., whether people have positive, neutral, or negative opinion towards products or services. In this paper presents a novel Dynamic Fuzzy approach based Bayesian Classification (DFBC) model to deal with the troubles in one go under a combined framework. This model represents each review document in the form of opinion pairs for sentiment detection. Meanwhile, the proposed system processed meaningful tweets into clusters using unsupervised machine learning technique such as DFBC.


Sentiment Analysis, Bayesian Classification, Twitter, LDA.

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