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Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses

Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses

Kaiquan S. J. Xu, Wei Wang, Jimmy Ren, Jin S. Y. Xu, Long Liu, Stephen Liao
Copyright: © 2011 |Volume: 7 |Issue: 1 |Pages: 14
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781613507933|DOI: 10.4018/jiit.2011010101
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MLA

Xu, Kaiquan S. J., et al. "Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses." IJIIT vol.7, no.1 2011: pp.1-14. http://doi.org/10.4018/jiit.2011010101

APA

Xu, K. S., Wang, W., Ren, J., Xu, J. S., Liu, L., & Liao, S. (2011). Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses. International Journal of Intelligent Information Technologies (IJIIT), 7(1), 1-14. http://doi.org/10.4018/jiit.2011010101

Chicago

Xu, Kaiquan S. J., et al. "Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses," International Journal of Intelligent Information Technologies (IJIIT) 7, no.1: 1-14. http://doi.org/10.4018/jiit.2011010101

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Abstract

With the Web 2.0 paradigm, a huge volume of Web content is generated by users at online forums, wikis, blogs, and social networks, among others. These user-contributed contents include numerous user opinions regarding products, services, or political issues. Among these user opinions, certain comparison opinions exist, reflecting customer preferences. Mining comparison opinions is useful as these types of viewpoints can bring more business values than other types of opinion data. Manufacturers can better understand relative product strengths or weaknesses, and accordingly develop better products to meet consumer requirements. Meanwhile, consumers can make purchasing decisions that are more informed by comparing the various features of similar products. In this paper, a novel Support Vector Machine-based method is proposed to automatically identify comparison opinions, extract comparison relations, and display results with the comparison relation maps by mining the volume of consumer opinions posted on the Web. The proposed method is empirically evaluated based on consumer opinions crawled from the Web. The initial experimental results show that the performance of the proposed method is promising and this research opens the door to utilizing these comparison opinions for business intelligence.

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