Antomatic Product Feature Extraction for Efficient Analysis of Product Reviews Using Term Statistics


KIPS Transactions on Software and Data Engineering, Vol. 16, No. 6, pp. 497-502, Jun. 2009
10.3745/KIPSTB.2009.16.6.497, Full Text:

Abstract

In this paper, we introduce an automatic product feature extracting system that improves the efficiency of product review analysis. Our system consists of 2 parts: a review collection and correction part and a product feature extraction part. The former part collects reviews from internet shopping malls and revises spoken style or ungrammatical sentences. In the latter part, product features that mean items that can be used as evaluation criteria like 'size' and 'style' for a skirt are automatically extracted by utilizing term statistics in reviews and web documents on the Internet. We choose nouns in reviews as candidates for product features, and calculate degree of association between candidate nouns and products by combining inner association degree and outer association degree. Inner association degree is calculated from noun frequency in reviews and outer association degree is calculated from co-occurrence frequency of a candidate noun and a product name in web documents. In evaluation results, our extraction method showed an average recall of 90%, which is better than the results of previous approaches.


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Cite this article
[IEEE Style]
W. C. Lee, H. A. Lee and K. J. Lee, "Antomatic Product Feature Extraction for Efficient Analysis of Product Reviews Using Term Statistics," KIPS Journal B (2001 ~ 2012) , vol. 16, no. 6, pp. 497-502, 2009. DOI: 10.3745/KIPSTB.2009.16.6.497.

[ACM Style]
Woo Chul Lee, Hyun Ah Lee, and Kong Joo Lee. 2009. Antomatic Product Feature Extraction for Efficient Analysis of Product Reviews Using Term Statistics. KIPS Journal B (2001 ~ 2012) , 16, 6, (2009), 497-502. DOI: 10.3745/KIPSTB.2009.16.6.497.