mlsanityData to 14 Oct 2026

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

arXiv 2211.03923 ↗First posted 8 Nov 2022cs.CL cs.IR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 8 Nov 2022.

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11 of 34 references.

References · 11

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  5. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
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  7. 2020UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Trainingcited by 204
  8. 2019Fine-grained Sentiment Classification using BERTcited by 38
  9. 2020DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterancescited by 36
  10. 2021BERT-CoQAC: BERT-based Conversational Question Answering in Contextcited by 13
  11. 2020A systematic literature review on machine learning applications for consumer sentiment analysis using online reviewscited by 5

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