论文标题
部分可观测时空混沌系统的无模型预测
Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions
论文作者
论文摘要
在这项工作中，我们提出了一个框架，仅依靠基于聊天的客户支持（CS）交互来预测单个用户的建议决定。在我们的案例研究中，我们分析了拉丁美洲一家大型电子商务公司的财务垂直行业中的16.4万用户和48.7万个客户支持对话。因此，我们的主要贡献和目标是使用自然语言处理（NLP）来评估和预测建议行为，除了使用静态情感分析外，我们还利用了每个用户情感动态的预测能力。我们的结果表明，凭借各自的功能可解释性，可以仅基于其CS对话的信息情感演变来预测用户推荐产品或服务的可能性。
In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
