Aspect-based Sentiment Analysis: A Bibliometric Review using Bibliometrix to Map Research Trends and Algorithm Methods

Abstract

This study presents a bibliometric overview of research trends and algorithmic models in Aspect-Based Sentiment Analysis (ABSA). Data were collected from the Scopus database, resulting in a dataset of 2,344 journal articles published between 2021 and early 2026. The analysis was conducted using the Bibliometrix and Biblioshiny packages in R to perform number of publications per year, source’s production over time, country production over time, keyword co-occurrence, thematic mapping and evolution of research themes. The results show that ABSA research has experienced rapid growth with an annual publication increase of more than 30%. This study identifies BERT algorithmic models and Graph Convolutional Networks (GCN) as the most dominant supporting tools in the research literature. Thematic maps show that transformer-based techniques and attention mechanisms have emerged as key driving themes in this field. Furthermore, thematic evolution maps reveal a shift in focus from technical aspect extraction to online public opinion analysis, reinforced by the sharp surge in the use of Large Language Models (LLMs) in recent years. The findings provide a structured overview of the intellectual landscape of ABSA, clarifying dominant research clusters, methodological trajectories, and emerging themes. By highlighting the central role of transformer architectures, graph-based neural networks, and LLM integration, this study offers methodological guidance for future model development. Furthermore, the bibliometric insights reduce research fragmentation and identify underexplored directions, offering valuable insights for researchers to identify research gaps and develop more advanced ABSA models in future studies.