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A Survey on Unsupervised Joint Topic Modeling Approach in Bayesian Model

S. Indhu, S. R. Lavanya


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.


Microblog, Bayesian, Topic Modeling, LDA, OTM.

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D.M. Blei, A.Y. Ng, and M.I. Jordan, “Latent Dirichlet Allocation,” J. Machine Learning Research, vol. 3, pp.993-1022, 2003.

B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury, “Micro-blogging as online word of mouth branding,” in Proc. Extended Abstr. Human Factors Comput. Syst., 2009, pp. 3859–3864.

J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011.

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.

M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment in twitter events,” J. Am. Soc. Inform. Sci. Technol., vol. 62, no. 2, pp. 406–418, 2011.

Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of twitter data,” in Proc. Workshop Lang. Soc. Media, 2011, pp. 30–38.

Liu, “Sentiment analysis and opinion mining,” Synthesis Lect. Human Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.

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.

Ounis, C. Macdonald, J. Lin, and I. Soboroff, “Overview of the trec-2011 microblog track,” in Proc. 20th Text Retrieval Conf., 2011,

Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Computer Science Department, Stanford, USA, pp. 1–12, 2009.

S. Li, C.-R. Huang, G. Zhou, and S. Y. M. Lee, “Employing personal/impersonal views in supervised and semi-supervised sentiment classification,” in Proc. 48th Annu. Meeting Assoc. Comput. Linguistics, 2010, pp. 414–423.

X. Wan, “Co-training for cross-lingual sentiment classification,” in Proc. Joint Conf. 47th Annu. Meeting ACL 4th Int. Joint Conf. Natural Language Process. AFNLP: Volume 1-Volume 1, 2009, pp. 235–243.

K. Bennett and A. Demiriz, “Semi-supervised support vector machines,” in Proc. Adv. Neural Inform. Proc. Syst., 1999, pp. 368–374.

T. Hofmann. Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning, 42(1):177–196, 2001.

Chenghua Lin, Yulan He, and Richard Everson. 2010. A comparative study of bayesian models for unsupervised sentiment detection. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, CoNLL ’10, pages 144–152, Stroudsburg, PA, USA. Association for Computational Linguistics.

Taras Zagibalov and John Carroll. 2008. Automatic seed word selection for unsupervised sentiment classification of chinese text. In Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1, COLING ’08, pages1073–1080, Stroudsburg, PA, USA. Association for Computational Linguistics.


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