Extraction of comparative opinionate sentences from product online reviews

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.