公共行为和情绪的相关性 采矿是由新闻机构的视角驱动的
IEEE transactions on neural networks and learning systems
|August 23, 2024
概括
这项研究揭示了公众情绪,如恐惧和悲伤,如何驱动特定的行为,如恐慌. 了解这些情绪-行为联系为公共安全和人机交互提供了洞察力.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 情绪显著影响人类行为,但公众情绪和行为之间的直接相关性尚未得到充分探索.
- 了解这些联系对于破译行为动机和社会事件动态至关重要.
研究的目的:
- 通过使用每日新闻数据,提出一种新的方法来挖掘公众行为和情绪之间的相关性.
- 基于假设上下文 (KRHC) 开发一种知识表示模型,以揭示隐含的情感-行为关系.
主要方法:
- 从新闻数据中提取和概括方面-情感-反应 (A-E-R) 三胞胎.
- 开发一个具有自我反射机制的KRHC模型,并注意捕捉隐性模式.
- 整合基于规则的方法,用于明确的关系和深度学习,用于隐含的关系.
主要成果:
- 该KRHC模型有效地揭示隐含的情绪-行为关系.
- 对A-E-R和KINSHIP数据集的验证证明了该模型的有效性.
- 确定了特定的情绪-行为相关性,例如,恐惧,悲伤和惊喜与恐慌行为相关.
结论:
- 拟议的方法提供了一个强大的框架来分析公共情绪-行为动态.
- 结果为人机交互和公共安全管理的应用提供了宝贵的见解.
- 这项研究强调了分析公众情绪对于预测和理解集体行为的重要性.
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