基于相互注意力机制的表情符号多式联络微博情绪分析
Yinxia Lou1, Junxiang Zhou2, Jun Zhou3
1School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China.
Scientific reports
|November 26, 2024
概括
这项研究引入了一种新模型,该模型使用emoji视觉图像来更好地分析社交媒体上的情绪. 多式联络方法显著提高了通过表情符号表达的情绪理解的准确性.
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
- 社交媒体分析
背景情况:
- 在像新浪微博这样的社交媒体平台上,表情符号对于传达情绪和意见至关重要.
- 当前的情感分析方法往往忽略了表情符号中的丰富视觉数据,将它们视为仅仅是符号或文本.
- 这种局限性阻碍了微博环境中的精确情绪检测和分析.
研究的目的:
- 为增强的表情符号微博情绪分析提出一种新的多式联络信息整合模型.
- 有效地利用表情符号内在的视觉信息来进行更细微的情感检测.
- 为了解决忽视表情符号视觉数据的现有方法的局限性.
主要方法:
- 开发了一种专门用于表情符号情绪分析的多式联络信息整合模型.
- 整合了一个文本-emoji视觉互关注机制,以利用emoji的视觉特征.
- 在手动注释的微博数据集上进行了实验.
主要成果:
- 与没有使用emoji视觉信息的基线模型相比,拟议的模型表现出更好的性能.
- 在宏观F1得分上获得了1.37%的提高,精度增加了2.30%.
- 这些发现强调了整合视觉表情符号数据用于情感分析的重要性.
结论:
- 这种新的多式模式有效地捕获和利用表情符号的视觉信息进行情感分析.
- 整合视觉表情符号数据显著提高了微博上的情绪分析准确性和有效性.
- 这项研究为未来的多式联络情绪分析研究提供了宝贵的资源.
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