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Indhu, S.
- Sentiment Analysis on Twitter Using Dynamic Fuzzy Approach
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Authors
S. Indhu
1,
S. R. Lavanya
1
Affiliations
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
Source
Fuzzy Systems, Vol 10, No 3 (2018), Pagination: 53-56Abstract
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.Keywords
Sentiment Analysis, Bayesian Classification, Twitter, LDA.
References
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- A Survey on Unsupervised Joint Topic Modeling Approach in Bayesian Model
Abstract Views :179 |
PDF Views:2
Authors
S. Indhu
1,
S. R. Lavanya
1
Affiliations
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
Source
Biometrics and Bioinformatics, Vol 10, No 4 (2018), Pagination: 66-69Abstract
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.
Keywords
Microblog, Bayesian, Topic Modeling, LDA, OTM.References
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