Adjusting Chatbot Conversation to User Personality and Mood
Adjusting Chatbot Conversation to User Personality and Mood
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根据用户个性和情绪调整聊天机器人对话
DOI：
10.1007/978-3-030-61641-0_3
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
B. Galitsky
中科院分区：
文献类型：
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作者：
B. Galitsky
由于会话式 CRM 系统与人类客户而不是其他计算机系统进行通信，因此它们需要以优化聊天机器人会话结果的方式处理人类情绪。聊天机器人需要理解同伴的情绪并产生不仅符合他们的情绪状态而且还试图改进其解决问题的话语。我们构建了一个模型和来自各种来源的情感和个性的训练数据集，以便在情感空间中对客户做出正确的反应并引导他度过这个过程。我们评估了启用情感计算的聊天机器人的总体贡献，并观察到客户认为响应的相关性提高了 18%。
As conversational CRM systems communicate with human customers and not other computer systems, they need to tackle human emotions in a way to optimize the outcome of a chatbot session. A chatbot needs to understand the emotions of its peers and produce utterances which not only match their emotional states but also attempt to improve it towards solving a problem. We construct a model and a training dataset of emotions and personality from various sources to properly react to the customer in the emotional space and to navigate him through it. We evaluated an overall contribution of a chatbot enabled with affective computing and observed up to 18% boost in the relevance of responses, as perceived by customers.