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http://dx.doi.org/10.3745/KIPSTB.2009.16B.6.497

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

Lee, Woo-Chul ((주) 유승토탈솔류션)
Lee, Hyun-Ah (금오공과대학교 컴퓨터공학부)
Lee, Kong-Joo (충남대학교 전기정보통신공학부)
Publication Information
The KIPS Transactions:PartB / v.16B, no.6, 2009 , pp. 497-502 More about this Journal
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.
Keywords
Product Review; Product Feature Extraction; Feature Based Summarization; Term Statistics; Electronic Commerce;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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