Comprehensive helpfulness of online reviews: a dynamic strategy for ranking reviews by intrinsic and extrinsic helpfulness

Jindong Qin*, Pan Zheng, Xiaojun Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Information overload often makes it difficult for consumers to identify valuable online reviews through the traditional “helpful votes” button in the big data era, so it is essential to locate helpful reviews. Unlike the existing efforts that often measure online reviews’ helpfulness one-sidedly, this study takes the intrinsic helpfulness (IH) and extrinsic helpfulness (EH) into account, and the intrinsic-extrinsic comprehensive helpfulness (ICH-ECH) plot can be constructed by ensemble neural network model (ENNM) and time-weighted standard deviation accordingly. Furthermore, this study proposes a measure of EH ignored by previous studies, that is, the percentage of negative replies, which contain useful information that can measure online reviews helpfulness. We corrected it with a time sliding window by an improved iterative Bayesian probability approach (IBPA). In addition, this study further proposes a dynamic time-aware helpfulness ranking (DTAHR) model to dynamically rank reviews and identify beneficial reviews in a short time. We used real data sets from JD.com to conduct all experiments. The experimental results show that the performance of the DTAHR model is significantly better than other strategies. Our findings offer guidelines to evaluate the helpfulness of online reviews from multiple perspectives and rank them dynamically.

Original languageEnglish
Article number113859
Number of pages14
JournalDecision Support Systems
Volume163
Early online date28 Aug 2022
DOIs
Publication statusPublished - Dec 2022

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (NSFC) under Projects 71701158 and 72071151 , MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJC630114), and the Natural Science Foundation of Hubei Province (2020CFB773).

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Dynamic ranking strategy
  • Ensemble neural network model (ENNM)
  • Extrinsic helpfulness (EH)
  • Intrinsic helpfulness (IH)
  • Online reviews

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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