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In this study, we introduce a Temporal-Spatial Semantic Fusion Network (TSSFN) for processing Aspect-Based Sentiment Analysis (ABSA) tasks. Our proposed model incorporates a Global Time Semantic Network (GTSN) for comprehensive feature extraction, and a Dual Relevance Attention (DRA) mechanism to discern interactions between context and aspects. The performance of our model was tested on five well-known ABSA datasets, achieving promising results in analyzing sentiment at the aspect level.

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Temporal-Spatial Semantic Fusion Network for Aspect-Based Sentiment Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Xinyang Tong,
  • Jiawei Zhou,
  • Ying Xing,
  • Bin Yang

摘要

In this study, we introduce a Temporal-Spatial Semantic Fusion Network (TSSFN) for processing Aspect-Based Sentiment Analysis (ABSA) tasks. Our proposed model incorporates a Global Time Semantic Network (GTSN) for comprehensive feature extraction, and a Dual Relevance Attention (DRA) mechanism to discern interactions between context and aspects. The performance of our model was tested on five well-known ABSA datasets, achieving promising results in analyzing sentiment at the aspect level.