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A Survey on Sentiment Analysis on Product Reviews

IJCA Proceedings on National Conference on Advances in Communication and Computing
© 2015 by IJCA Journal
NCACC 2015 - Number 1
Year of Publication: 2015
Suvarna D. Tembhurnikar
Nitin N. Patil

Suvarna D Tembhurnikar and Nitin N Patil. Article: A Survey on Sentiment Analysis on Product Reviews. IJCA Proceedings on National Conference on Advances in Communication and Computing NCACC 2015(1):22-24, September 2015. Full text available. BibTeX

	author = {Suvarna D. Tembhurnikar and Nitin N. Patil},
	title = {Article: A Survey on Sentiment Analysis on Product Reviews},
	journal = {IJCA Proceedings on National Conference on Advances in Communication and Computing},
	year = {2015},
	volume = {NCACC 2015},
	number = {1},
	pages = {22-24},
	month = {September},
	note = {Full text available}


This paper presents a survey of sentiments analysis for product review. Online social and news media has become a very popular for users to share their opinions and generate prosperous and timely information about real world events of all kinds. Several efforts were dedicated for mining opinions and sentiments automatically from natural language in social media messages, news and commercial product reviews. For this task a deep understanding of the explicit and implicit information are needed. Social media like facebook, twitter, online review websites like Amazon are popular sites where millions of users exchange their opinions and making it a valuable platform for tracking and analyzing public sentiments. This provides important information for decision making in various domains. A lot of research has been done on modeling and tracking public sentiment. Here main focus is given to interpret sentiment variations. It has been observed that emerging topics within the sentiment variation periods are greatly related to the actual reasons behind the variations. In this paper we are discussing LDA based model for interpreting sentiments. This model is used for giving rank to the tweets with respect to their popularity within the variation period. This method efficiently finds foreground topics and rank reason candidates and also used to find topic differences between two sets of documents.


  • Shulong Tan, Yang Li, Huan Sun, Ziyu Guan, Xifeng Yan, Jiajun Bu, Chun Chen and Xiaofei He, Interpreting the Public Sentiment Variations on Twitter, IEEE Transactions On Knowledge And Data Engineering, Vol. 6, No. 5, May 2013.
  • B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, From tweets to polls: Linking text sentiment to public opinion time series, in Proc. 4th Int. AAAI Conf. Weblogs Social Media, Washington, DC, USA, 2010.
  • M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, Sentiment strength detection in short informal text, J. Amer. Soc. Inform. Sci. Technol. , Vol. 61, No. 12, pp. 2544–2558, 2010.
  • Y. Tausczik and J. Pennebaker, The psychological meaning of words: Liwc and computerized text analysis methods, J. Lang. Soc. Psychol. , vol. 29, no. 1, pp. 24–54, 2010.
  • B. Pang and L. Lee, Opinion mining and sentiment analysis, Found. Trends Inform. Retrieval, vol. 2, no. (1–2), pp. 1–135, 2008.
  • G. Mishne and N. Glance, "Predicting movie sales from blogger sentiment," in Proc. AAAI-CAAW, Stanford, CA, USA, 2006.
  • A. 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 Social Media, Washington, DC, USA, 2010.
  • D. Chakrabarti and K. Punera, "Event summarization using tweets," in Proc. 5th Int. AAAI Conf. Weblogs Social Media, Barcelona, Spain, 2011.
  • Y. Hu, A. John, F. Wang, and D. D. Seligmann, Et-lda: Joint topic modeling for aligning events and their twitter feedback, in Proc. 26th AAAI Conf. Artif. Intell. , Vancouver, BC, Canada, 2012.
  • D. Tao, X. Li, X. Wu, and S. J. Maybank, Geometric mean for subspace selection, IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, no. 2, pp. 260–274, Feb. 2009.
  • X. Tian, D. Tao, and Y. Rui, Sparse transfer learning for interactive video search reranking, ACM Trans. Multimedia Comput. Commun. Appl. , vol. 8, no. 3, article 26, Jul. 2012.