Trends and Developments in Aspect-Based Sentiment Analysis: A Bibliometric Study Using CiteSpace and the Web of Science Database (2010-2025)

With the rapid advancement of artificial intelligence and natural language processing (NLP), aspect-based sentiment analysis (ABSA) has emerged as a crucial subfield of fine-grained sentiment analysis, widely applied in e-commerce, public opinion monitoring, and intelligent customer service. This study employs bibliometric analysis to systematically examine research trends in ABSA from 2010 to 2025, utilizing data retrieved from the Web of Science core collection and analyzed through CiteSpace. The findings reveal a significant increase in ABSA research since 2019, with deep learning models such as BiLSTM, CRF, and attention mechanisms driving major breakthroughs. Additionally, the study identifies leading countries, institutions, and influential authors in this field, highlighting the shift from traditional sentiment lexicons and machine learning approaches to deep learning-based methodologies. The insights presented in this paper contribute to a comprehensive understanding of the ABSA research landscape, offering valuable references for future academic and industrial applications.