以自然语言处理为基础的情绪和使用分析,用于支持的AI驱动聊天机器人
Keiichi Watanuki1,2, Ryoya Oba2, Naoki Nozawa2
1Advanced Institute of Innovative Technology, Saitama University, Saitama, Japan.
Epilepsia open
|December 11, 2025
概括
人工智能 (AI) 聊天机器人可以通过解决医疗问题和情感需求来支持治疗. 分析用户互动显示,主动学习可以增强积极的情绪,而自我反思可以减少它们.
科学领域:
- * 医疗保健中的人工智能
- * 自然语言处理 (NLP)
- *数字心理健康支持
背景情况:
- *人工智能驱动的聊天机器人越来越多地用于患者教育和心理健康支持.
- * 它们在治疗中的有效性尚未得到充分研究,因此需要对用户交互模式和情绪影响进行调查.
研究的目的:
- * 检查用户与以为重点的聊天机器人 (EpiloBot) 之间的基于文本的交互.
- * 通过NLP技术了解对话模式和情绪趋势.
- * 评估聊天机器人对用户情绪和参与度的影响.
主要方法:
- *对23名参与者的文字互动进行分析 (10名患者,10名护理人员,3名医疗保健专业人员).
- *使用JMedRoBERTa将消息分类为内容集群,并使用DeBERTa进行情绪转移跟踪.
- *采用UMAP集群和k-means用于查询差异化;情绪分析专注于频繁使用者 (n=5).
主要成果:
- *确定了两个主要内容集群:"医学调查" (与治疗相关) 和"探索性使用" (个人/情感).
- *情感分析表明,主动学习信息与增加的积极情绪相关.
- *内省性,自我评价性信息与积极性降低有关.
结论:
- *NLP对聊天机器人交互的分析提供了关于患者参与和治疗中的情感支持需求的见解.
- *聊天机器人应该平衡医疗指导与慢性疾病管理的心理支持.
- * 未来的人工智能开发应该优先考虑对环境有意识的同情反应,以优化患者的治疗结果.
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