通过预测对话中未来情绪，帮助客服提升客户满意度。
ERFC: Happy Customers with Emotion Recognition and Forecasting in Conversation in Call Centers
- 融合多模态与上下文依赖，预测对话中各方情绪走向。
- 在IEMOCAP数据集上实现更高情绪预测准确率。
- 适合呼叫中心、客户服务等需实时情绪干预的场景。
对话中的情绪识别在呼叫中心分析、意见挖掘、金融、零售、医疗等领域具有广泛应用。在呼叫中心场景中，客服不仅需接听电话，还需通过保持中性或积极情绪来缓解客户不满。由于对话中一方情绪常受另一方影响，客服若能预判客户未来情绪并适时回应，可有效提升客户体验，将不悦客户转化为满意客户。为此，我们提出新型对话情绪识别与预测架构ERFC，该架构融合多模态信息、情绪属性、上下文及对话双方话语间的依赖关系。在IEMOCAP数据集上的大量实验验证了其可行性，为呼叫中心等以客户满意度为核心的应用带来显著商业价值。
原文摘要 · Abstract (English)
Emotion Recognition in Conversation has been seen to be widely applicable in call center analytics, opinion mining, finance, retail, healthcare, and other industries. In a call center scenario, the role of the call center agent is not just confined to receiving calls but to also provide good customer experience by pacifying the frustration or anger of the customers. This can be achieved by maintaining neutral and positive emotion from the agent. As in any conversation, the emotion of one speaker is usually dependent on the emotion of other speaker. Hence the positive emotion of an agent, accompanied with the right resolution will help in enhancing customer experience. This can change an unhappy customer to a happy one. Imparting the right resolution at right time becomes easier if the agent has the insight of the emotion of future utterances. To predict the emotions of the future utterances we propose a novel architecture, Emotion Recognition and Forecasting in Conversation. Our proposed ERFC architecture considers multi modalities, different attributes of emotion, context and the interdependencies of the utterances of the speakers in the conversation. Our intensive experiments on the IEMOCAP dataset have shown the feasibility of the proposed ERFC. This approach can provide a tremendous business value for the applications like call center, where the happiness of customer is utmost important.
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