| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 553453 | Decision Support Systems | 2014 | 12 Pages |
•We propose hybrid text regression models for predicting online review helpfulness.•We creatively adapt RFM analysis to characterize a reviewer's online engagement.•A reviewer's RFM dimensions improve the prediction of review helpfulness.•The hybrid approach, combining bag-of-words and RFM, produces the best results.•Our study can help online platforms rate and present new online reviews instantly.
The era of Web 2.0 is witnessing the proliferation of online social media platforms, which develop new business models by leveraging user-generated content. One rapidly growing source of user-generated data is online reviews, which play a very important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. In this paper, we develop and compare several text regression models for predicting the helpfulness of online reviews. In addition to using review words as predictors, we examine the influence of reviewer engagement characteristics such as reputation, commitment, and current activity. We employ a reviewer's RFM (Recency, Frequency, Monetary Value) dimensions to characterize his/her overall engagement and investigate if the inclusion of those dimensions helps improve the prediction of online review helpfulness. Empirical findings from text mining experiments conducted using reviews from Yelp and Amazon offer strong support to our thesis. We find that both review text and reviewer engagement characteristics help predict review helpfulness. The hybrid approach of combining the textual features of bag-of-words model and RFM dimensions produces the best prediction results. Furthermore, our approach facilitates the estimation of the helpfulness of new reviews instantly, making it possible for social media platforms to dynamically adjust the presentation of those reviews on their websites.
