Differentiation in Online Product Reviews: A Machine Learning Based Analysis
By Gu Tianyu, Yong Liu, Madhu Viswanathan
Information Systems Research
Citation
Gu Tianyu., Yong Liu., Viswanathan, Madhu. (2020). Differentiation in Online Product Reviews: A Machine Learning Based Analysis Information Systems Research .
Copyright
Information Systems Research, 2020
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Abstract
Online review of products and services has become a prevalent information source for consumers. This paper examines a key aspect of reviews and reviewer behavior: whether and how the content of a review systematically differs from reviews posted earlier. Content differentiation is particularly important when more reviews are posted as newer star ratings tend to converge providing limited room for the newer reviews to stand out. The authors apply machine learning and deep learning to classify restaurant reviews on Yelp.com. Employing first-difference models that account for dynamic panel bias, the analysis provides strong evidence for review differentiation: when previous reviews write more about the food (or non-food) dimension, a later review tends to write less about it. Review differentiation is greater as more reviews are published, when the star rating associated with the review deviates more from previous star ratings, and for regular (versus established) reviewers. The authors also show that review differentiation helps enhance the impact of a review. These findings suggest two important but distinct motivations for review differentiation: to enable a review (and its reviewer) to stand out from the crowd and to provide support for star ratings. Implications for reviews and review platforms are discussed.

Madhu Viswanathan is Associate Professor of Marketing at the Indian School of Business (ISB) and Executive Director of the Srini Raju Centre for IT and the Networked Economy (SRITNE). His research examines how emerging technologies reshape markets and how firms, platforms, and policymakers can design institutions that foster innovation, trust, and sustainable economic growth.

Working at the intersection of marketing, economics, strategy, and information systems, his research seeks to understand how technological change transforms consumer behaviour, organizational incentives, market design, and public policy. He studies how technologies such as artificial intelligence, digital platforms, creator ecosystems, and data-driven decision systems influence the way markets function and evolve. Across these diverse settings, his work aims to develop insights that help organizations respond to technological change with strategies that are both economically effective and socially meaningful.

His research has been published in leading journals including the Journal of Marketing Research, Marketing Science, and the Journal of Economics & Management Strategy. He serves on the Editorial Review Boards of the Journal of Marketing Research and Production and Operations Management and has been recognized with the Outstanding Reviewer Award from the Journal of Marketing Research.

As Executive Director of SRITNE, Madhu leads the Centre’s research and engagement on artificial intelligence, digital markets, and technology-enabled growth. Under his leadership, SRITNE is building a multidisciplinary research agenda around AI, digital platforms, creator economies, and emerging technologies while strengthening collaborations across academia, industry, startups, and government. His vision is to position the Centre as a leading hub for research that bridges technological innovation with business strategy and public policy.

Alongside his research, he teaches MBA, doctoral, and executive education programmes on growth strategy, digital marketing, artificial intelligence, and technology-enabled business. His teaching combines rigorous research with practical decision frameworks that help managers navigate growth, digital transformation, and strategic decision-making in rapidly evolving markets.

Beyond academia, Madhu serves as an independent director and advisor to technology ventures, working with organizations on growth strategy, platform businesses, AI adoption, digital transformation, and technology-enabled market design. His advisory work reflects his broader commitment to translating rigorous academic research into practical solutions for business leaders, entrepreneurs, and policymakers.

Prior to joining ISB, he was on the faculty at the University of Arizona. He received his PhD in Marketing from the University of Minnesota and his undergraduate degree from BITS Pilani. His work seeks to bridge rigorous academic research with business practice and public policy, helping organizations harness emerging technologies to build more innovative, efficient, and inclusive markets.

Prof Madhu Viswanathan
Madhu Viswanathan