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Online Review Mining for Forecasting Sales

S. Keerthana, J. Jaishree

Abstract


The growing popularity of online product review forums invites people to express opinions and sentiments toward the products .It gives the knowledge about the product as well as sentiment of people towards the product. These online reviews are very important for forecasting the sales performance of product. In this paper, we discuss the online review mining techniques in movie domain. Sentiment PLSA which is responsible for finding hidden sentiment factors in the reviews and ARSA model used to predict sales performance. An Autoregressive Sentiment and Quality Aware model (ARSQA) also in consideration for to build the quality for predicting sales performance. We propose clustering and classification based algorithm for sentiment analysis.


Keywords


Online Review Mining, Text Mining, Reviews, S-PLSA, ARSA, Clustering, Classification.

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References


D. Gruhl, R. Guha, R. Kumar, J.Novak,”The predictive power of online chatter,” Proc.11th ACM SIGKDD Int’l Conf. Knowledge discovery in data mining (KDD),pp.78-87,2005.

A. Ghose and P.G. Ipeirotis, Designing Novel Review Ranking Systems: “Predicting the usefulness and impact of reviews,” Prog. Ninth Int’I Conf. Electronic commerce (ICEC) pp.303-310 2007.

Y.Liu, X. yu, X. Hung, “The predicting power of sentiments,” Data Mining for Business Application, pp.183-195, 2009.

L. Cao, Y. Zhao, H. Zhang and E.K. Park, “Flexible Frameworks for Actionable Knowledge Discovery,” IEEE Trans. Knowledge and DTA data eng pp.1299-1232 sep. 2009.

Xiaohui Yu,” Mining Online Reviews for Predicting Sales Performance: A case study in the movie domain.” IEEE Trans. Knowledge and data eng., vol 24, no. 4 apr 2012


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