Mining the determinants of review helpfulness: a novel approach using intelligent feature engineering and explainable AI

Abstract

Purpose

This paper aims to find determinants that can predict the helpfulness of online customer reviews (OCRs) with a novel approach.

Design/methodology/approach

The approach consists of feature engineering using various text mining techniques including BERT and machine learning models that can classify OCRs according to their potential helpfulness. Moreover, explainable artificial intelligence methodologies are used to identify the determinants for helpfulness.

Findings

The important result is that the boosting-based ensemble model showed the highest prediction performance. In addition, it was confirmed that the sentiment features of OCRs and the reputation of reviewers are important determinants that augment the review helpfulness.

Research limitations/implications

Each online community has different purposes, fields and characteristics. Thus, the results of this study cannot be generalized. However, it is expected that this novel approach can be integrated with any platform where online reviews are used.

Originality/value

This paper incorporates feature engineering methodologies for online reviews, including the latest methodology. It also includes novel techniques to contribute to ongoing research on mining the determinants of review helpfulness.

Citation

Kim, J., Lee, H. and Lee, H. (2023), "Mining the determinants of review helpfulness: a novel approach using intelligent feature engineering and explainable AI", Data Technologies and Applications, Vol. 57 No. 1, pp. 108-130. https://doi.org/10.1108/DTA-12-2021-0359