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.

本文提出了一个基于即时聊天的客户支持 (CS) 互动的框架，用于预测个体用户的推荐决策。通过分析一家拉丁美洲大型电子商务公司金融垂直领域内的 16.4k 用户和 48.7k 客户支持对话，本研究的主要贡献和目标是利用自然语言处理（NLP）来评估和预测推荐行为，在使用静态情感分析的同时利用每个用户的情感动态的预测能力。结果表明，通过信息级别的情感演变，可以完全自动化地预测用户推荐产品或服务的概率以及相关的功能解释。