107个面部表情符号的情感,语义,频率和描述规范
Tatjana Scheffler1, Ivan Nenchev2
1Department for German Language and Literature, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany. tatjana.scheffler@rub.de.
Behavior research methods
|August 15, 2024
概括
这项研究提出了一个新的面部表情符号规范数据集,包括情绪评级和使用频率. 表情符号的熟悉程度与使用和更清晰的含义相关,增强了数字通信中的语义理解.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 人与计算机的交互
背景情况:
- 在数字通信中,表情符号越来越普遍.
- 现有的表情符号数据集缺乏全面的情感和语义规范.
- 了解表情符号的含义对于认知和语言研究至关重要.
研究的目的:
- 为面部表情符号创建一个新的情感,语义和描述规范的数据集.
- 分析表情符号的主观评分和使用频率.
- 为语义分析开发基于描述的表情符号矢量嵌入.
主要方法:
- 收集了来自138名德语发言人的所有面部表情符号的主观评分 (价值,兴奋,熟悉性,清晰度,视觉复杂性).
- 从Twitter和WhatsApp公司收集了表情符号使用频率数据.
- 从参与者提供的表情符号描述中生成矢量嵌入.
主要成果:
- 复制了表情符号的兴奋和价值之间的二次关系,类似于单词.
- 在表情符号熟悉度,使用频率,情感价值和清晰度之间发现了强烈的相关性.
- 基于描述的表情符号矢量嵌入有效捕捉语义关系并超越现有模型.
结论:
- 开发的表情符号规范数据集提供了高可靠性和有效性.
- 这一资源将推动研究emojis.is的认知处理和语言特性.
- 这些发现对受控实验设计和表情符号研究的未来有影响.
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