A Novel Approach to Predict the Helpfulness of Online Reviews

Date
2020-01-07
Authors
Namvar, Morteza
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Online reviews help consumers reduce uncertainty and risks faced in purchase decision making by providing information about products and services. However, the overwhelming amount of data continually being produced in online review platforms introduce a challenge for customers to read and judge the reviews. This research addresses the problem of misleading and overloaded information by developing a novel approach to predict the helpfulness of online reviews. The proposed approach in this study, first, clusters reviews using reviewer-related, and temporal factors. It then uses review-related factors to predict online review helpfulness in each cluster. Using a sample of Amazon.com reviews, the empirical findings offer strong support to the proposed approach and show its superior predictions of review helpfulness compared to earlier approaches. The outcomes of this study help customers in online shopping and assist online retailers in reducing information overload to improve their customers’ experience.
Description
Keywords
Social Media Management in Big Data Era, analytics, big data, consumer decision making, helpfulness, online reviews, sentiment analysis
Citation
Rights
Access Rights
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.