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Sentiment Analysis on Twitter Using Dynamic Fuzzy Approach
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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.
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- Hu .M and B. Liu, “Mining and Summarizing Customer Reviews,” Proc. 10th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’04), pp. 168-177, 2004.
- B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proc. ACL-02 Conf. Empirical Methods Natural Language Process., 2002, pp. 79–86.
- Shen .D, J. Wu, B. Cao, J.-T. Sun, Q. Yang, Z. Chen, and Y. Li, “Exploiting Term Relationship to Boost Text Classification,” Proc. 18th ACM Conf. Information and Knowledge Management (CIKM ’09), pp. 1637-1640, 2009.
- Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, “Predicting elections with Twitter: What 140 characters reveal about political sentiment,” in Proc. 4th Int. AAAI Conf. Weblogs Soc. Media, 2010, vol. 10, pp. 178–185.
- L. T. Nguyen, P. Wu, W. Chan, W. Peng, and Y. Zhang, “Predicting collective sentiment dynamics from time-series social media,” in Proc. 1st Int. Workshop Issues Sentiment Discovery Opinion Mining, 2012, p. 6.
- L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proc. 49th Annu. Meet. Assoc. Comput. Linguistics Human Language Technol., 2011, pp. 142–150.
- Yang and C. Cardie, “Context-aware learning for sentence-level sentiment analysis with posterior regularization,” in Proc. 52nd Annu. Meet. Assoc. Comput. Linguistics, 2014, pp. 325–335.
- M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, Mar. 2003.
- S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 751–760.
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