Identifying comparative customer requirements from product online reviews for competitor analysis. (March 2016)

• Record Type: Journal Article
• Authors: Jin, Jian; Ji, Ping; Gu, Rui
• Is Part Of: Engineering applications of artificial intelligence. Volume 49(2016:Jan.)
• Page Start: 61
• Page End: 73
• Publication Date: 2016-03
• Subjects: Customer requirement -- Review analysis -- Competitor analysis -- Product comparison -- Representative yet comparative sentences -- Product design; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.12.005
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: A large 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 opinionated sentences associated with some specific features from product online reviews has been investigated by some researchers. However, few of them examined how to employ these valuable resources for competitor analysis. Hence, in this research, a framework is illustrated to select pairs of opinionated representative yet comparative sentences with specific product features from reviews of competitive products. With the help of the techniques on sentiment analysis, opinionated sentences referring to a specific feature are first identified from product online reviews. Then, information representativeness, information comparativeness and information diversity are investigated for the selection of a small number of representative yet comparative opinionated sentences. 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.