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Article type: Research Article
Authors: Tian, Huaqianga | Yu, Longb; * | Tian, Shengweia | Long, Junc | Zhou, Tiejund | Wang, Boa | Li, Yuhuana
Affiliations: [a] School of Software, Xinjiang University, Xinjiang, China | [b] Network and Information Center, Xinjiang University, Xinjiang, China | [c] School of Information Science and Engineering, Central South University, Changsha, China | [d] Xinjiang Internet Information Center, Xinjiang, China
Correspondence: [*] Corresponding author. Long Yu, Network and Information Center, Xinjiang University, Xinjiang, China. E-mail: yul@xju.edu.cn.
Abstract: Aspect-Based Sentiment Analysis (ABSA) has been the focus of increasing study in recent years. Previous research has demonstrated that incorporating syntactic information, such as dependency trees, can enhance ABSA performance. Despite the widespread use of metaphors in daily life to express emotions more vividly, few studies have integrated this literary device into ABSA. In this paper, we propose a novel ABSA model that utilizes Metaphor Identification Procedure (MIP) to encode both the sentence and aspect word as a single unit, thereby overcoming these limitations. Our experimental results demonstrate that our model achieves competitive performance in ABSA.
Keywords: Aspect-based sentiment analysis, metaphorical sentiment analysis, transformer, deep learning
DOI: 10.3233/JIFS-233077
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8065-8074, 2024
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