Retrieving Minority Product Reviews Using Positive/Negative Skewness


KIPS Transactions on Software and Data Engineering, Vol. 4, No. 3, pp. 121-128, Mar. 2015
10.3745/KTSDE.2015.4.3.121,   PDF Download:

Abstract

A given product`s online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews` sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.


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Cite this article
[IEEE Style]
H. R. Cho and J. S. Lee, "Retrieving Minority Product Reviews Using Positive/Negative Skewness," KIPS Transactions on Software and Data Engineering, vol. 4, no. 3, pp. 121-128, 2015. DOI: 10.3745/KTSDE.2015.4.3.121.

[ACM Style]
Hee Ryon Cho and Jong Seok Lee. 2015. Retrieving Minority Product Reviews Using Positive/Negative Skewness. KIPS Transactions on Software and Data Engineering, 4, 3, (2015), 121-128. DOI: 10.3745/KTSDE.2015.4.3.121.