Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics
New York University (NYU) - Leonard N. Stern School of Business
Panagiotis G. Ipeirotis
New York University - Leonard N. Stern School of Business
January 24, 2010
IEEE Transactions on Knowledge and Data Engineering, 2011
With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. Further, reviews that rate products negatively can be associated with increased product sales when the review text is informative and detailed.By using Random Forest based classiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: 'reviewer-related' features, 'review subjectivity' features, and 'review readability' features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.
Number of Pages in PDF File: 25
Keywords: Internet commerce, social media, user-generated content, textmining, word-of-mouth, product reviews, economics, sentiment analysis, online communities.
JEL Classification: M3, L13, L14, L86, D40, D43
Date posted: September 1, 2008 ; Last revised: June 19, 2014
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