Tianzhi Zhang1, Gang Zhou1,*, Shuang Zhang2, Shunhang Li1, Yepeng Sun1, Qiankun Pi1, Shuo Liu3
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 279-305, 2025, DOI:10.32604/cmc.2024.055943
- 03 January 2025
Abstract Joint Multimodal Aspect-based Sentiment Analysis (JMASA) is a significant task in the research of multimodal fine-grained sentiment analysis, which combines two subtasks: Multimodal Aspect Term Extraction (MATE) and Multimodal Aspect-oriented Sentiment Classification (MASC). Currently, most existing models for JMASA only perform text and image feature encoding from a basic level, but often neglect the in-depth analysis of unimodal intrinsic features, which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features. Given this problem, we propose a Text-Image Feature Fine-grained… More >
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