A computational analysis of aspect-based sentiment analysis research through bibliometric mapping and topic modeling
Article
Chen, Xieling, Xie, Haoran, Tao, Xiaohui, Wang, Fu Lee, Zhang, Dian and Dai, Hong-Ning. 2025. "A computational analysis of aspect-based sentiment analysis research through bibliometric mapping and topic modeling." Journal of Big Data. 12 (1). https://doi.org/10.1186/s40537-025-01068-y
Article Title | A computational analysis of aspect-based sentiment analysis research through bibliometric mapping and topic modeling |
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ERA Journal ID | 210802 |
Article Category | Article |
Authors | Chen, Xieling, Xie, Haoran, Tao, Xiaohui, Wang, Fu Lee, Zhang, Dian and Dai, Hong-Ning |
Journal Title | Journal of Big Data |
Journal Citation | 12 (1) |
Article Number | 40 |
Number of Pages | 34 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2196-1115 |
Digital Object Identifier (DOI) | https://doi.org/10.1186/s40537-025-01068-y |
Web Address (URL) | https://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01068-y |
Abstract | With the rising volume of public and consumer engagement on social media platforms, the field of aspect-based sentiment analysis (ABSA) has garnered substantial attention. ABSA contains the systematic extraction of aspects, the analysis of associated sentiments, and the temporal evolution of these sentiments. Researchers have responded to the burgeoning interest by innovating new methodologies and strategies to address specific research challenges, thereby navigating complex scenarios and evolving challenges within ABSA. While existing reviews on ABSA encompass strategies, methods, and applications utilizing survey methodologies, a conspicuous gap exists in literature specifically addressing the development of methodologies and topics and their interaction in ABSA. Furthermore, the application of topic modeling and keyword co-occurrence has been limited in the extant literature. This study conducts a comprehensive overview of the ABSA field by leveraging bibliometrics, topic modeling, social network analysis, and keyword co-occurrence analysis to scrutinize 1325 ABSA research articles spanning the years 2009 to 2023. The analyses encompass research themes and topics, scientific collaborations, top publication sources, research areas, institutions, countries/regions, and publication and citation trends. Beyond examining and contrasting the connections between research topics and methodologies, this study identifies emerging trends and hotspots, providing researchers with insight into technical directions, limitations, and future research regarding ABSA topics and methodologies. |
Keywords | Aspect-based sentiment analysis; Literature review; Computational analysis; Bibliometric mapping; Topic modeling; Social network visualization |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Byline Affiliations | Guangzhou University, China |
Lingnan University of Hong Kong, China | |
School of Mathematics, Physics and Computing | |
Hong Kong Metropolitan University, China | |
Shenzhen University, China | |
Hong Kong Baptist University, China |
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https://research.usq.edu.au/item/zx103/a-computational-analysis-of-aspect-based-sentiment-analysis-research-through-bibliometric-mapping-and-topic-modeling
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