在社交媒体文本中评估情绪分类方案:基于注释的方法
Fa Zhang1, Jian Chen2, Qian Tang2
1Beijing Institute of Technology, Zhuhai School, No.6, JinFeng Road, Zhuhai, Guangdong Province, 519088, China. richter2000@163.com.
BMC psychology
|September 28, 2024
概括
埃克曼的六种基本情绪方案最适合于社交媒体文本分析,尽管它需要更多的积极情绪类别. 这项研究评估了公共活动文本的五种情感方案.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 社会科学 社会科学 社会科学
背景情况:
- 社交媒体文本分析提供了对公众心理状态和社会现象的洞察.
- 情感是复杂的,有各种分类方案,需要适当的选择来进行文本分析.
- 确定社交媒体的最佳情绪分类方案对于准确的分析至关重要.
研究的目的:
- 提出和应用一个框架来评估社交媒体文字分析的情感分类方案.
- 确定最适合的情感方案来分析与公共事件相关的新浪微博帖子.
- 根据定义的标准,比较五种不同的情绪分类方案.
主要方法:
- 根据五个标准制定了一个框架:稳固性,覆盖范围,协议,紧性和区别.
- 分析层次过程 (AHP) 从手动注释实验中合成了定性和定量因素.
- 分析了2848条新浪微博的帖子,使用五个方案:SemEval,Ekman,七种情绪,Plutchik和GoEmotions.
主要成果:
- 埃克曼的六种基本情绪方案在AHP评估中获得了最高分.
- 多维缩放 (MDS) 揭示了Ekman,Plutchik和七种情感方案之间的相似之处.
- 对应分析显示Ekman的情绪与其他方案的情绪之间存在不同程度的对齐,一些GoEmotions类别缺乏明确的相关性.
结论:
- 埃克曼的六种基本情绪模式是评估集体中最有效的.
- 该研究强调了Ekman方案的局限性,特别是它对社交媒体数据的积极情绪类别不足.
- 拟议的框架为在文本分析中选择适当的情感分类方案提供了一个强大的方法.
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