Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.

本研究提出了一种针对Twitter客服对话数据的的细粒度对话行为分类方法，并使用序列SVM-HMM模型在实时中预测不同行为类型，最终以数据驱动的方法预测客服对话数据的重要结果，包括客户满意度、客户沮丧和解决问题的整体情况。研究结果显示，对话中某些行为类型和位置会对产生的结果产生显著影响，为人工客服平台提供了可用的指导规则。

我能帮助您吗？”：使用细粒度的对话行为模型Twitter客服对话