好,坏和矛盾:通过计算模型推断38000多个中文单词的情感价值
Tianqi Wang1,2,3, Xu Xu4
1School of Foreign Languages, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China.
Behavior research methods
|November 16, 2023
概括
这项研究开发了一个计算神经网络,用于预测38000多个中文单词的情感词评分 (价值和兴奋). 该模型准确估计了单词影响和个体评分变化,有助于心理语言学研究.
科学领域:
- 计算语言学计算语言学
- 心理语言学 心理语言学
- 自然语言处理自然语言处理.
背景情况:
- 情感词评分 (价值和兴奋) 对各种研究领域至关重要.
- 现有的情感规范通常在规模上是有限的,特别是对研究较少的语言.
- SUBTLEX-CH数据库提供单词频率数据,但缺乏全面的情感规范.
研究的目的:
- 为大量的中文单词推断出情感价值 (价值和兴奋).
- 预测单个单词的人类情感评级的变化程度.
- 提供一个计算模型,将语义表示与情感属性联系起来.
主要方法:
- 实现了一个计算神经网络,以建模基于词向量的语义表示和情感概率密度之间的关系.
- 利用概率密度函数来预测单词的价值和激发值.
- 根据模型的输出,估计了情感评级的变化程度.
主要成果:
- 计算模型成功地预测了情感价值 (价值和兴奋),这些价值与人类评级在很大程度上趋同.
- 该模型对情感变异性的估计捕捉了人类评级中观察到的个体差异的重要方面.
- 超过38000个中文单词的外推情感值和变化度被释放.
结论:
- 计算模型可以有效地推断出情感词规范,并估计评级变化.
- 这种方法提高了研究情感词汇数据库的规模和范围.
- 这些发现通过计算情感语义学的透视来提供了对体现认知的洞察.
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