Deciphering Word-of-Mouth in Social Media
Text-Based Metrics of Consumer Reviews
Is there any business value in consumer-generated product reviews? How are consumer opinions correlated with product sales? Does a product sell well when consumer opinions converge or diverge? This research attempts to answer these questions.
Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions — i.e., through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics thereof influence consumer purchases and product sales.
By integrating marketing theories with text mining techniques, the authors propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, they conduct an empirical study based on data from Amazon.com and bn.com (Barnes and Noble). The results demonstrate significant effects of the proposed measures on product sales. This effect is not fully captured by non-textual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, their divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures in the literature.
The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, the results suggest that, when managing social media, firms should pay special attention to the textual content information, and more importantly focus on the right measures.
Zhu Zhang is with the Department of MIS and Yubo Chen is with the Department of Marketing at the Eller College of Management, University of Arizona. Xin Li is with the Department of Information Systems, City University of Hong Kong.
Published in ACM Transactions on Management Information Systems, March, 2012.