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文本在图像中增强的自我监督对齐模型用于基于方面的多模式情绪分析在社交媒体上

Xuefeng Zhao¹, Yuxiang Wang¹, Zhaoman Zhong¹

¹School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China.

Sensors (Basel, Switzerland)

|April 26, 2025

概括

本研究介绍了TESAM,这是一种基于方面的多式联络情绪分析 (ABMSA) 的新型模型,可以有效地分析社交媒体内容. TESAM通过将图像中发现的文本结合起来来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:

社交媒体的增长需要先进的论挖掘和多式联络数据的情绪分析.
基于方面的多式联络情绪分析 (ABMSA) 对于细粒度的情绪决定至关重要.
由于忽视了嵌入式图像文本,现有的ABMSA方法在社交媒体数据上扎.

研究的目的:

为ABMSA开发一个全面分析多式联运信息的模型.
通过整合从图像中提取的文本来提高情感分析的准确性.
为了减少噪音干扰,并改善对相关语义特征的关注.

主要方法:

提出了文字在图像中增强的自我监督对齐模型 (TESAM).
使用光学字符识别 (OCR) 来从图像中提取嵌入文本.
融合了提取的文本与视觉特征,并纳入了侧面词来进行集中分析.
采用自主监督的对齐预训练,以弥合使用欧几里德距离和等号相似度的模式之间的语义差距.

主要成果:

在ABMSA的三个基准指标上,TESAM表现出色.
该模型有效地整合了视觉和文本信息,包括嵌入的图像文本.
整合面词成功地减少了无关的语义特征的干扰.

结论:

拟议的TESAM模型显著改进了基于方面的多式联络情绪分析对社交媒体数据.
整合嵌入式图像文本和面向导向的特征选择可以提高情绪分析的准确性.
自主监督的对齐有效地减轻了不同模式之间的语义差距.

关键词:

基于方面的多式联运情绪分析.模式调整模式的调整.这是光学字符识别系统.自主监督学习学习社交媒体社交媒体

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