Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76619
Title: Extraction of comparative opinionate sentences from product online reviews
Authors: Ji, P 
Jin, J 
Keywords: Competitor analysis
Customer requirement
Product comparison
Product design
Review analysis
Text mining
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015, Zhangjiajie, China, 15 - 17 August 2015, 7382216, p. 1777-1785 How to cite?
Abstract: Big volume of product online reviews are generated from time to time, which contain rich information regarding customer requirements. These reviews help designers to make exhaustive analyses of competitors, which is one indispensable step in market-driven product design. How to extract critical opinionate sentences associated with some specific features from product online reviews has been investigated by some researchers. However, few of them examined how to select a small number of representative yet comparative sentences for competitor analysis. In this research, a framework is illustrated to select pairs of opinionate sentences referring to a specific feature from reviews of competitive products. With the help of the techniques on sentiment analysis, opinionate sentences referring to a specific feature are first identified from product online reviews. Then, for the selection of a small number of representative yet comparative opinionate sentences, information representativeness, information comparativeness and information diversity are investigated. Accordingly, an optimization problem is formulated, and three greedy algorithms are proposed to analyze this problem for suboptimal solutions. Finally, with a large amount of real data from Amazon.com, categories of extensive experiments are conducted and the final encouraging results are realized, which prove the effectiveness of the proposed approach.
URI: http://hdl.handle.net/10397/76619
ISBN: 9781467376822
DOI: 10.1109/FSKD.2015.7382216
Appears in Collections:Conference Paper

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