Discovering business intelligence from online product reviews: A rule-induction framework

  • Authors:

  • Wingyan Chung;Tzu-Liang (Bill) Tseng

  • Affiliations:

  • UNC Fayetteville State University, School of Business and Economics, 1200 Murchison Road, Fayetteville, NC 28301, USA;Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

Online product reviews are a major source of business intelligence (BI) that helps managers and marketers understand customers' concerns and interests. The large volume of review data makes it difficult to manually analyze customers' concerns. Automated tools have emerged to facilitate this analysis, however most lack the capability of extracting the relationships between the reviews' rich expressions and the customer ratings. Managers and marketers often resort to manually read through voluminous reviews to find the relationships. To address these challenges, we propose the development of a new class of BI systems based on rough set theory, inductive rule learning, and information retrieval methods. We developed a new framework for designing BI systems that extract the relationship between the customer ratings and their reviews. Using reviews of different products from Amazon.com, we conducted both qualitative and quantitative experiments to evaluate the performance of a BI system developed based on the framework. The results indicate that the system achieved high accuracy and coverage related to rule quality, and produced interesting and informative rules with high support and confidence values. The findings have important implications for market sentiment analysis and e-commerce reputation management.