The online review plays an important role as electronic word-of-mouth (eWOM) for potential consumers to make informed purchase decisions. However, the large number of reviews poses a considerable challenge because it is impossible for customers to read all of them for reference. Moreover, there are different types of online reviews with distinct features, such as numeric ratings, text descriptions, and comparative words, for example; such heterogeneous information leads to more complexity for customers. In this paper, we propose a method to integrate such rich and heterogeneous information. The integrated information can be classified into two categories: descriptive information and comparative information. The descriptive information consists of online opinions directly given by consumers using text sentiments and numeric ratings to describe one specific product. The comparative information comes from comparative sentences that are implicitly embedded in the reviews and online comparative votes that are explicitly provided by third-party websites to compare more than one product. Both descriptive information and comparative information are integrated into a digraph structure, from which an overall eWOM score for each product and a ranking of all products can be derived. We collect both descriptive and comparative information for three different categories of products (mobile phones, laptops, and digital cameras) during a period of 10days. The results demonstrate that our method can provide improved performance compared with those of existing product ranking methods. A ranking system based on our method is also provided that can help consumers to compare multiple products and make appropriate purchase decisions effortlessly. An effective system is provided to help consumers rank multiple products.A unified eWOM ranking model is proposed to integrate heterogeneous information.The comprehensive descriptive and comparative information is integrated.The comparative votes are employed to enhance the comparative information.
['Xian Yang', 'Guangfei Yang', 'Jiangning Wu']
Integrating rich and heterogeneous information to design a ranking system for multiple products