Mining Online Reviews in Websites for Predicting Sales Performance

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Paper Details

Paper Title

Mining Online Reviews in Websites for Predicting Sales Performance

Authors

Divagar S, Joesph Raymond V

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Abstract

Nowadays people posting reviews are increasing and it gives us the knowledge to buy the products. People can express their sentiments through reviews posted in websites. By these reviews we can predict the movie sales performance. The question is how the sentiment factor can be analyzed in the reviews? Reviews are analyzed by Sentiment PLSA which helps us to find the unknown factors in the reviews and helps us to classify the reviews. Then we have to predict the sales performance, for the prediction regression is suitable way to do and we suggest an autoregressive sentiment (ARSA) Aware Model. To improve the prediction accuracy to enhance the best quality factor we also consider an autoregressive sentiment and quality aware model (ASQA)to build the quality for mining reviews sales performance in movie domain.

Keywords- OnlineReview mining, hidden sentiment analysis, prediction, S-PLSA, ARSA

Publication Details

Unique Identification Number - IJEDR1401211
Page Number(s) - 1184-1186
Pubished in - Volume 2 | Issue 1 | March 2014
DOI (Digital Object Identifier) -
Publisher - IJEDR (ISSN - 2321-9939)

Cite this Article

Divagar S, Joesph Raymond V, "Mining Online Reviews in Websites for Predicting Sales Performance", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 1, pp.1184-1186, March 2014, Available at :http://www.ijedr.org/papers/IJEDR1401211.pdf

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