计算机科学
知识图
图形
利用
情绪分析
构造(python库)
领域(数学)
人工智能
异构网络
情报检索
机器学习
数据挖掘
理论计算机科学
电信
无线网络
计算机安全
数学
纯数学
无线
程序设计语言
作者
Yujie Wan,Yuzhong Chen,Jian‐Di Lin,Jiayuan Zhong,Chen Dong
标识
DOI:10.1016/j.csl.2023.101587
摘要
Aspect-level multimodal sentiment analysis has also become a new challenge in the field of sentiment analysis. Although there has been significant progress in the task based on image-text data, existing works do not fully deal with the implicit sentiment expression in data. In addition, they don’t fully exploit the important information from external knowledge and image tags. To address these problems, we propose a knowledge-augmented heterogeneous graph convolutional network (KAHGCN). First, we propose a dynamic knowledge selection algorithm to select the most relevant external knowledge, thereby enhancing KAHGCN’s ability of understanding the implicit sentiment expression in review texts. Second, we propose a graph construction strategy to construct a heterogeneous graph that aggregates review text, image tags and external knowledge. Third, we propose a multilayer heterogeneous graph convolutional network to strengthen the interaction between information from external knowledge, review texts and image tags. Experimental results on two datasets demonstrate the effectiveness of the KAHGCN.
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