Start Date
12-13-2015
Description
Online word-of-mouth in the form of online reviews and ratings is an increasingly important resource for consumers to acquire product information for their purchase decision. However, dimensional review bias, originated from consumer heterogeneity and their multidimensional product preferences and experiences, have been shown to undermine the information transfer among consumers. Through a novel text mining approach, we identify and quantify two types of dimensional bias from textual reviews: dimensional preference bias and dimensional rating bias. We also introduce a quantitative method to mitigate the dimensional rating bias. We examined the effectiveness and applicability of our bias measures and de-bias method in the context of multi-dimensional and single-dimensional rating systems. Specifically, we focused on the hotel reviews on TripAdvisor.com and Expedia.com. Our preliminary results showed promising theoretical and managerial contributions.
Recommended Citation
Ge, Yong and Li, Jingjing, "Measure and Mitigate the Dimensional Bias in Online Reviews and Ratings" (2015). ICIS 2015 Proceedings. 18.
https://aisel.aisnet.org/icis2015/proceedings/DecisionAnalytics/18
Measure and Mitigate the Dimensional Bias in Online Reviews and Ratings
Online word-of-mouth in the form of online reviews and ratings is an increasingly important resource for consumers to acquire product information for their purchase decision. However, dimensional review bias, originated from consumer heterogeneity and their multidimensional product preferences and experiences, have been shown to undermine the information transfer among consumers. Through a novel text mining approach, we identify and quantify two types of dimensional bias from textual reviews: dimensional preference bias and dimensional rating bias. We also introduce a quantitative method to mitigate the dimensional rating bias. We examined the effectiveness and applicability of our bias measures and de-bias method in the context of multi-dimensional and single-dimensional rating systems. Specifically, we focused on the hotel reviews on TripAdvisor.com and Expedia.com. Our preliminary results showed promising theoretical and managerial contributions.