From Ratings to Consumer Preferences: A Bibliometric Review of Customer Review Research in the Digital Age

Fikri Kurniawan; Agung Utama; Tony Wijaya

doi:10.55927/fjmr.v5i7.131

Authors

Fikri Kurniawan Yogyakarta State University
Agung Utama Yogyakarta State University
Tony Wijaya Yogyakarta State University

DOI:

https://doi.org/10.55927/fjmr.v5i7.131

Keywords:

Customer Review, Customer Preference, Machine Learning, E-Commerce, Consumer Behavior

Abstract

This study provides a comprehensive bibliometric review of the development of research on customer reviews and customer preferences in the digital era. Based on an analysis of 108 Scopus-indexed publications from 2005 to 2025, the study maps the evolution of themes, research trends, scientific collaboration, and intellectual landscapes, illustrating the shift in focus from rating-based analysis to predictive approaches for understanding consumer preferences. Using VOSviewer, six main clusters were identified: Customer-Centric Product Design & Preference Modelling, E-Commerce Customer Review Analytics & Performance Evaluation, Sentiment Analysis & Personalization in Customer Reviews, AI-Driven Recommendation & Feature-Based Review Analysis, Consumer Decision-Making & Online Purchasing Behavior, and Machine Learning & Text Mining for Customer Satisfaction Analysis. Research trends indicate a shift from rating-based analysis to more predictive consumer-preference modeling, driven by AI, machine learning, and NLP in processing customer reviews in the digital era. The findings show that sentiment analysis, machine learning, data mining, and natural language processing dominate current scholarly discourse, affirming the role of customer reviews as a strategic data source for mapping preferences and developing more adaptive recommendation systems. This study contributes to understanding the transformation of research from numerical evaluation to consumer preference analysis based on big data in the digital age.

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