Using Bayesian Network to Predict Online Review Helpfulness

Sangjae Lee, Kun Chang Lee and Joon Yeon Choeh
Additional contact information
Sangjae Lee: College of Business Administration, Sejong University, Seoul 05006, Korea
Kun Chang Lee: Department of Global Administration, SKK Business School/SAIHST (Samsung Advanced Institute for Health Sciences & Technology), Sungkyunkwan University, Seoul 03063, Korea
Joon Yeon Choeh: Department of Software, Sejong University, Seoul 05006, Korea

Sustainability, 2020, vol. 12, issue 17, 1-17

Abstract: The enormous volume and largely varying quality of available reviews provide a great obstacle to seek out the most helpful reviews. While Naive Bayesian Network (NBN) is one of the matured artificial intelligence approaches for business decision support, the usage of NBN to predict the helpfulness of online reviews is lacking. This study intends to suggest HPNBN (a helpfulness prediction model using NBN), which adopts NBN for helpfulness prediction. This study crawled sample data from Amazon website and 8699 reviews comprise the final sample. Twenty-one predictors represent reviewer and textual traits as well as product traits of the reviews. We investigate how the expanded list of predictors including product, reviewer, and textual characteristics of eWOM (online word-of-mouth) has an effect on helpfulness by suggesting conditional probabilities of the binned determinants. The prediction accuracy of NBN outperformed that of the k-nearest neighbor (kNN) method and the neural network (NN) model. The results of this study can support determining helpfulness and support website design to induce review helpfulness. This study will help decision-makers predict the helpfulness of the review comments posted to their websites and manage more effective customer satisfaction strategies. When prospect customers feel such review helpfulness, they will have a stronger intention to pay a regular visit to the target website.

Keywords: Naive Bayesian network; online review; helpfulness prediction; helpfulness; predictors of helpfulness (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/17/6997/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/17/6997/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:17:p:6997-:d:405054

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().