Integrating rich and heterogeneous information to design a ranking system for multiple products

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.

[1]  Zeshui Xu,et al.  Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making , 2015, IEEE Transactions on Fuzzy Systems.

[2]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[3]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[4]  Jianbin Hu,et al.  Customer Reviews for Individual Product Feature-based Ranking , 2011, 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control.

[5]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[6]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[7]  Alok N. Choudhary,et al.  Voice of the Customers: Mining Online Customer Reviews for Product Feature-based Ranking , 2010, WOSN.

[8]  Arthur B. Markman,et al.  Processing Product Unique Features , 2001 .

[9]  Songbo Tan,et al.  Building domain-oriented sentiment lexicon by improved information bottleneck , 2009, CIKM.

[10]  John Sutton,et al.  Product differentiation and industrial structure , 1985 .

[11]  Wilfried R. Vanhonacker,et al.  The Challenge for Multinational Corporations in China: Think Local, Act Global , 2007 .

[12]  Chang Fu-yang.  Chinese Comparative Sentences Identification and Comparative Relations Extraction , 2009 .

[13]  Jacob Goldenberg,et al.  Mine Your Own Business: Market-Structure Surveillance Through Text Mining , 2012, Mark. Sci..

[14]  Stephen Shaoyi Liao,et al.  Mining comparative opinions from customer reviews for Competitive Intelligence , 2011, Decis. Support Syst..

[15]  Bing Liu,et al.  Opinion Extraction and Summarization on the Web , 2006, AAAI.

[16]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[17]  Bing Liu,et al.  Identifying comparative sentences in text documents , 2006, SIGIR.

[18]  Natalie S. Glance,et al.  Star Quality: Aggregating Reviews to Rank Products and Merchants , 2010, ICWSM.

[19]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[20]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[21]  Bin Gu,et al.  Research Note - The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products , 2012, Inf. Syst. Res..

[22]  T. S. Raghu,et al.  Cyberinfrastructure for homeland security: advances in information sharing, data mining, and collaboration systems , 2004 .

[23]  Bin Gu,et al.  Informational Cascades and Software Adoption on the Internet: An Empirical Investigation , 2008, MIS Q..

[24]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[25]  Yue Lu,et al.  Latent aspect rating analysis on review text data: a rating regression approach , 2010, KDD.

[26]  Zhu Zhang,et al.  Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth , 2013, TMIS.

[27]  Meng Wang,et al.  Product comparison using comparative relations , 2011, SIGIR.

[28]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[29]  Juan Feng,et al.  Rising or Dropping: the Consumer Review-oriented Pricing Paradox , 2011, ICIS.

[30]  Ming Liu,et al.  Research of Product Ranking Technology Based on Opinion Mining , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[31]  Hsinchun Chen,et al.  Intelligence and security informatics: information systems perspective , 2006, Decis. Support Syst..

[32]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[33]  S. Li,et al.  The Influence of eWOM on Virtual Consumer Communities: Social Capital, Consumer Learning, and Behavioral Outcomes , 2007, Journal of Advertising Research.

[34]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[35]  S. Jang,et al.  Restaurant experiences triggering positive electronic word-of-mouth (eWOM) motivations. , 2011 .

[36]  Beibei Li,et al.  Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced Content , 2011, Mark. Sci..

[37]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[38]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[39]  Pradeep Chintagunta,et al.  The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets , 2010, Mark. Sci..

[40]  Bing Liu,et al.  Mining Comparative Sentences and Relations , 2006, AAAI.

[41]  Eric J. Johnson,et al.  A componential analysis of cognitive effort in choice , 1990 .

[42]  Yubo Chen,et al.  Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix , 2004, Manag. Sci..

[43]  Alissa Mariello.  The Five Stages of Successful Innovation , 2007 .

[44]  Z. John Zhang,et al.  From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts , 2007, Manag. Sci..

[45]  Chrysanthos Dellarocas,et al.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003, Manag. Sci..

[46]  Nan Hu,et al.  Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales , 2014, Decis. Support Syst..

[47]  A. Choudhary,et al.  Mining millions of reviews: a technique to rank products based on importance of reviews , 2011, ICEC '11.

[48]  E. S. Gopi,et al.  Probability And Random Process , 2007 .

[49]  Xiao Jianguo.  Learning to Identify Chinese Comparative Sentences , 2008 .