语义增强和交叉模式交互融合用于社交媒体中的情感分析
Guangyu Mu1,2, Ying Chen1, Xiurong Li3
1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.
PloS one
|April 28, 2025
概括
本研究引入了一个语义增强和交叉模式交互融合 (SECIF) 模型,以提高多模式情绪分析的准确性. SECIF模型有效地融合了文本和图像数据,优于现有的方法,以更好地了解公众论.
科学领域:
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 社交媒体的增长需要对公众论和趋势预测进行先进的情绪分析.
- 在情感分析中,模式融合面临着由于语义表示差异的挑战,导致噪音和精度降低.
- 现有的多式联运情绪分析模型难以有效地整合各种数据类型.
研究的目的:
- 提出一种新的语义增强和交叉模式交互融合 (SECIF) 模型,用于强大的多模式情绪分析.
- 通过增强语义表示和交叉模式交互来解决噪音,并提高情感分析中的分类准确性.
- 通过改进的多式联络情绪分析,使公众论和社会趋势能够更准确地理解.
主要方法:
- 使用BERT和ResNet来分别提取文本和图像特征表示.
- 提出了一个门式多头注意力 (GMHA) 机制,以聚合语义信息并减少噪音.
- 开发了一个交互式上下文化网络 (ICN) 模块,以增强文本特征表示和捕捉上下文依赖.
- 实现了一个交叉模式交互融合模块,以文本为主要功能和图像作为辅助功能.
- 使用交叉和KL分歧损失的组合优化模型性能.
主要成果:
- 与纯文本,纯图像和现有的多式模式相比,SECIF模型表现出更高的性能.
- 在平均精度方面取得了显著的改进:仅比文本精度提高了11.19%,仅比图像精度提高了82.27%,仅比多式模式精度提高了4.83%.
- 在公开可用的数据集上表现优于十个基线模型,精度增加了4.70%,F1得分增加了6.56%.
- 实验验证是在来自新浪微博公众论事件的数据集上进行的.
结论:
- SECIF模型有效地减轻了噪音,并在多式联络情绪分析中增强了语义理解.
- 拟议的方法通过深入整合文本和视觉特征,显著提高了情感分类的准确性和稳定性.
- 增强的多式联运情绪分析为政府提供了宝贵的见解,以了解公众的情绪并制定有效的策略.
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