Abstract
Aspect-based sentiment analysis aims to predict the polarity of sentiment towards a specific aspect in the context. In this paper, we propose the Temporal Semantic Attention Network (TSAN) model for ABSA tasks, which comprising a Global Semantic Feature Network for feature extraction and an Interact Dual Attention module to capture the dependencies of text-target interaction. Experiments on four ABSA benchmark datasets validates the effectiveness of our modules in extracting aspect-level sentiment features.
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Yang, B., Tong, X., Xing, Y., Shen, Q., Zhao, H., Xie, Z. (2023). Temporal Semantic Attention Network for Aspect-Based Sentiment Analysis. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_40
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DOI: https://doi.org/10.1007/978-3-031-39821-6_40
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