Determining banking service attributes from online reviews: text mining and sentiment analysis

Abstract

Purpose

The current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.

Design/methodology/approach

A total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.

Findings

The study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.

Research limitations/implications

The study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.

Practical implications

The study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.

Originality/value

This paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

Citation

Mittal, D. and Agrawal, S.R. (2022), "Determining banking service attributes from online reviews: text mining and sentiment analysis", International Journal of Bank Marketing, Vol. 40 No. 3, pp. 558-577. https://doi.org/10.1108/IJBM-08-2021-0380