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

► We developed a framework for extracting relationship between online reviews and user ratings. ► Three rule-induction techniques were used in a BI system to induce decision rules from Amazon reviews. ► The two Rough-Set Theory algorithms produced the most informative and interesting rules. ► ARM algorithm achieved the best scalability, efficiency, and representativeness of reviews. ► The research provides managers with new tools to support e-commerce reputation management. 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.