Scientists at New York University Detail Research in Data Engineering
Information Technology Newsweekly
© Copyright 2011 Information Technology Newsweekly via VerticalNews.com
According to the authors of recent research from New York City, New York, "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 reexamine 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. By using Random Forest-based classifiers, 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," wrote A. Ghose and colleagues, New York University.
The researchers concluded: "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."
Ghose and colleagues published their study in IEEE Transactions on Knowledge and Data Engineering (Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics. IEEE Transactions on Knowledge and Data Engineering, 2011;23(10):1498-1512).
For additional information, contact A. Ghose, New York University, Dept. of Informat Operat & Management Science, Leonarn N Stern School Business, New York City, NY 10012, United States.
Publisher contact information for the journal IEEE Transactions on Knowledge and Data Engineering is: Ieee Computer Society, 10662 Los Vaqueros Circle, PO Box 3014, Los Alamitos, CA 90720-1314, USA.
This article was prepared by Information Technology Newsweekly editors from staff and other reports. Copyright 2011, Information Technology Newsweekly via VerticalNews.com.