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Extracting customer knowledge from online consumer reviews: a collaborative-filtering-based opinion sentence identification approach
Conference proceeding

Extracting customer knowledge from online consumer reviews: a collaborative-filtering-based opinion sentence identification approach

Chin-Sheng Yang, Chih-Ping Wei and Christopher Yang
Proceedings of the 11th International Conference on electronic commerce, pp 64-71
12 Aug 2009
DOI: https://doi.org/10.1145/1593254.1593264

Abstract

collaborative filtering consumer reviews knowledge management opinion sentence identification sentiment analysis
Due to the popularity of online retail stores, consumers are not only shopping and comparing consumer products on the Web but also providing their consumer reviews on the Internet platform. The Web has become the largest repository of consumer reviews. Consumer reviews are beneficial to consumers, merchants, and manufacturers. Consumers may read the comments of other consumers and decide whether the product is good in the specific product features that they are interested in. For merchants or product manufacturers, consumer reviews help them understand general responses of customers on their products for product or marketing campaign improvement. In addition, consumer reviews can enable merchants better understand specific preferences of individual customers and facilitates effective marketing decisions. However, the large volume of consumer reviews makes it impossible for any individual consumer, merchant, or manufacturer to extract important knowledge efficiently. In this study, we concentrate on opinion sentence identification of focused sentiment analysis and propose a collaborative-filtering-based opinion sentence identification (CF-OSI) technique. The proposed CF-OSI technique considers opinion sentence identification as the sentence retrieval problem. In addition, a collaborative-filtering-based query expansion approach is incorporated into the CF-OSI technique to address possible effectiveness degradation caused by short user queries (i.e., limited number of query terms in query queries). Experiments have been conducted to empirically evaluate the effectiveness of our proposed technique. Our evaluation results show that the performance of our proposed CF-OSI technique is promising.

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8 citations in Scopus

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