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In this work, we propose a framework relying solely on chat-based customersupport (CS) interactions for predicting the recommendation decision ofindividual users. For our case study, we analyzed a total number of 16.4k usersand 48.7k customer support conversations within the financial vertical of alarge e-commerce company in Latin America. Consequently, our main contributionsand objectives are to use Natural Language Processing (NLP) to assess andpredict the recommendation behavior where, in addition to using staticsentiment analysis, we exploit the predictive power of each user's sentimentdynamics. Our results show that, with respective feature interpretability, itis possible to predict the likelihood of a user to recommend a product orservice, based solely on the message-wise sentiment evolution of their CSconversations in a fully automated way.

Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions