层次融合网络与增强的知识和对比学习,用于多模式的基于方面的情绪分析在社交媒体上
Xiaoran Hu1, Masayuki Yamamura1
1Department of Computer Science, School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama-shi 226-8502, Japan.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一种基于多模式方面情感分析 (MABSA) 的新层次框架,该框架利用图像标题和对比学习来弥合文本和图像之间的语义差距,提高情感分析的准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
背景情况:
- 基于多模式方面的情绪分析 (MABSA) 正在因智能设备和社交媒体的兴起而获得吸引力.
- 现有的MABSA方法往往忽视了文本和图像表示之间的语义差距,并忽视了像图像标题这样的外部知识.
研究的目的:
- 为MABSA提出一个名为HF-EKCL的新型层次框架,解决当前方法的局限性.
- 通过结合外部知识和改进跨模式特征融合来增强MABSA.
主要方法:
- 图片标题生成以补充文本和视觉特征.
- 多头交叉注意力和图形注意力神经网络,用于跨模态交互.
- 整合基于模式和基于标签的对比学习,以共享特征提取.
主要成果:
- 拟议的HF-EKCL框架有效地捕捉了元素级和结构级的信息,以实现多层面的方面融合.
- 相反的学习方法使模型能够学习与情绪相关的共享特征.
- 在两个Twitter数据集上的实验验验证了拟议模型的有效性.
结论:
- 通过有效地整合各种数据模式和外部知识,HF-EKCL框架为MABSA提供了重大进展.
- 该方法证明了情绪分析性能的提高,强调了解决语义差距和利用丰富的上下文信息的重要性.
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