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The Current State and Challenges of Aspect-Based Sentiment Analysis
- 1 School of Software, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China
* Author to whom correspondence should be addressed.
Abstract
Aspect-Based Sentiment Analysis (ABSA) is an important branch of natural language processing that aims to identify the sentiment polarity of aspect terms in the target language. With the increasing amount of text data generated by social media, e-commerce review platforms, and online forums, traditional holistic sentiment analysis can hardly meet the demand for fine-grained sentiment understanding. In comparison to conventional holistic sentiment analysis, ABSA provides a more comprehensive insight into the sentiment expressed. In addition, it has been widely employed in the fields of online public opinion analysis and management, thereby attracting increasing attention from researchers. This paper presents a comprehensive review of the existing literature on this topic, aiming to identify the principal research methods and findings in order to inform future research. In addition, it explores key research issues, including summarizing the theoretical underpinnings of ABSA, outlining the current dominant approaches to ABSA research, and finally exploring potential future developments and challenges in ABSA research. The results indicate that while significant advancements have been made, challenges such as handling implicit sentiments and integrating multimodal data still persist.
Keywords
Aspect-Based Sentiment Analysis, Deep Learning, Aspect Term Extraction, Sentiment Classification
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Cite this article
Yin,S. (2024). The Current State and Challenges of Aspect-Based Sentiment Analysis. Applied and Computational Engineering,114,25-31.
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