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Exploring the determinants of and predicting the helpfulness of online user reviews using decision trees

Exploring the determinants of and predicting the helpfulness of online user reviews using... PurposeThe purpose of this paper is to suggest important determinants for helpfulness from the reviews’ product data, review characteristics, and textual characteristics, and identify the more crucial factors among these determinants by using statistical methods. Furthermore, this study intends to propose a classification-based review recommender using a decision tree (CRDT) that uses a decision tree to identify and recommend reviews that have a high level of helpfulness.Design/methodology/approachThis study used publicly available data from Amazon.com to construct measures of determinants and helpfulness. To examine this, the authors collected data about economic transactions on Amazon.com and analyzed the associated review system. The final sample included 10,000 reviews composed of 4,799 helpful and 5,201 not helpful reviews.FindingsThe study selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics through using a t-test and logistics regression. The five important variables found to be significant in both t-test and logistic regression analysis were the total number of reviews for the product, the reviewer’s history macro, the reviewer’s rank, the disclosure of the reviewer’s name, and the length of the review in words. The decision tree method produced decision rules for determining helpfulness from the value of the product data, review characteristics, and textual characteristics. The prediction accuracy of CRDT was better than that of the k-nearest neighbor (kNN) method and linear multivariate discriminant analysis in terms of prediction error. CRDT can suggest better determinants that have a greater effect on the degree of helpfulness.Practical implicationsThe important factors suggested as affecting review helpfulness should be considered in the design of websites, as online retail sites with more helpful reviews can provide a greater potential value to customers. The results of the study suggest managers and marketers better understand customers’ review and increase the value to customers by proving enhanced diagnosticity to consumers.Originality/valueThis study is different from previous studies in that it investigated the holistic aspect of determinants, that is, product, review, and textual characteristics for classifying helpful reviews, and selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics by using a t-test and logistics regression. This study utilized a decision tree, which has rarely been used in predicting review helpfulness, to provide rules for identifying helpful online reviews. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Decision Emerald Publishing

Exploring the determinants of and predicting the helpfulness of online user reviews using decision trees

Management Decision , Volume 55 (4): 20 – May 15, 2017

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References (47)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0025-1747
DOI
10.1108/MD-06-2016-0398
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to suggest important determinants for helpfulness from the reviews’ product data, review characteristics, and textual characteristics, and identify the more crucial factors among these determinants by using statistical methods. Furthermore, this study intends to propose a classification-based review recommender using a decision tree (CRDT) that uses a decision tree to identify and recommend reviews that have a high level of helpfulness.Design/methodology/approachThis study used publicly available data from Amazon.com to construct measures of determinants and helpfulness. To examine this, the authors collected data about economic transactions on Amazon.com and analyzed the associated review system. The final sample included 10,000 reviews composed of 4,799 helpful and 5,201 not helpful reviews.FindingsThe study selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics through using a t-test and logistics regression. The five important variables found to be significant in both t-test and logistic regression analysis were the total number of reviews for the product, the reviewer’s history macro, the reviewer’s rank, the disclosure of the reviewer’s name, and the length of the review in words. The decision tree method produced decision rules for determining helpfulness from the value of the product data, review characteristics, and textual characteristics. The prediction accuracy of CRDT was better than that of the k-nearest neighbor (kNN) method and linear multivariate discriminant analysis in terms of prediction error. CRDT can suggest better determinants that have a greater effect on the degree of helpfulness.Practical implicationsThe important factors suggested as affecting review helpfulness should be considered in the design of websites, as online retail sites with more helpful reviews can provide a greater potential value to customers. The results of the study suggest managers and marketers better understand customers’ review and increase the value to customers by proving enhanced diagnosticity to consumers.Originality/valueThis study is different from previous studies in that it investigated the holistic aspect of determinants, that is, product, review, and textual characteristics for classifying helpful reviews, and selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics by using a t-test and logistics regression. This study utilized a decision tree, which has rarely been used in predicting review helpfulness, to provide rules for identifying helpful online reviews.

Journal

Management DecisionEmerald Publishing

Published: May 15, 2017

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