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Title: Product characteristic weighting for designer from online reviews: An ordinal classification approach
Authors: Jin, J.
Ji, P.
Liu, Y. 
Keywords: Opinion mining
Product review
Review analysis
Issue Date: 2012
Source: Jin, J.,Ji, P.,Liu, Y. (2012). Product characteristic weighting for designer from online reviews: An ordinal classification approach. ACM International Conference Proceeding Series : 33-40. ScholarBank@NUS Repository.
Abstract: Online product reviews are a reliable source of customers' sentiments. Directly connecting with customers and designers, online reviews can shorten product development life cycles. The problem arising is, although different techniques for processing online reviews are developed, the techniques are rarely seen on accelerating the design work ows. This paper proposes a two stage framework to learn the importance of characteristics from online reviews which could benefit product design. The first stage is a supervised learning routine to identify product characteristics. This procedure calculates the surrounding words' posterior probability. A linear weight learning algorithm is subsequently shown to reach the product characteristics identification. The second stage focus on meeting customers' needs. Distinct from existing classification and rank algorithms, this stage informs an ordinal classification algorithm to balance the weight of product characteristics. This algorithm depicts a pairwise approach to achieve ordinal classification. Furthermore, an integer none linear programming model is advised, which targets at recovering pairwise results to the original class for each object. Four brands of printer reviews from Amazon and real analysis from two experienced product designers are employed in this experimental study. The results validate the feasibility of the two stage framework, and the possibility to explore targeted models from online reviews for product designers. Copyright 2012 ACM.
Source Title: ACM International Conference Proceeding Series
ISBN: 9781450311434
DOI: 10.1145/2320765.2320784
Appears in Collections:Staff Publications

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