Deciphering Word-of-Mouth in Social Media : Text-Based Metrics of Consumer Reviews

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number5
Journal / PublicationACM Transactions on Management Information Systems
Volume3
Issue number1
Publication statusPublished - Apr 2012

Abstract

Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures 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 amanagerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures.

Research Area(s)

  • Consumer reviews, Design, Performance, Sentiment analysis, Social media, Text mining, Word-of-mouth