@inproceedings{gangopadhyay-mehrabani-2026-real,
title = "Real-Time Mitigation of Negative Emotion in Customer Care Calls",
author = "Gangopadhyay, Surupendu and
Mehrabani, Mahnoosh",
editor = "Barnes, Jeremy and
Barriere, Valentin and
De Clercq, Orph{\'e}e and
Klinger, Roman and
Nouri, C{\'e}lia and
Nozza, Debora and
Singh, Pranaydeep",
booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wassa-1.19/",
pages = "252--264",
ISBN = "979-8-89176-378-4",
abstract = "Speech emotion recognition (SER) is a compelling yet challenging research area with substantial practical relevance, particularly in enhancing human{--}machine interaction. Despite considerable progress in the field, the scarcity of realistic datasets that reflect real-world conditions makes it difficult to analyze system behavior in practice and can lead to degraded performance in industrial applications. In this study, we propose a system that detects negative emotions at each turn in a conversation by leveraging both linguistic and acoustic features. The approach is evaluated on real-world data, with a particular focus on identifying and responding to negative emotion in customer support scenarios. Designed for real-time application, the system is suitable for live deployment in call center environments. Furthermore, we propose an effective prompting strategy for using large language models (LLMs) as annotators, generating labeled data used to fine-tune small language models that achieve performance on par with the LLM used for annotation, while remaining suitable for real-time deployment."
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%0 Conference Proceedings
%T Real-Time Mitigation of Negative Emotion in Customer Care Calls
%A Gangopadhyay, Surupendu
%A Mehrabani, Mahnoosh
%Y Barnes, Jeremy
%Y Barriere, Valentin
%Y De Clercq, Orphée
%Y Klinger, Roman
%Y Nouri, Célia
%Y Nozza, Debora
%Y Singh, Pranaydeep
%S The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-378-4
%F gangopadhyay-mehrabani-2026-real
%X Speech emotion recognition (SER) is a compelling yet challenging research area with substantial practical relevance, particularly in enhancing human–machine interaction. Despite considerable progress in the field, the scarcity of realistic datasets that reflect real-world conditions makes it difficult to analyze system behavior in practice and can lead to degraded performance in industrial applications. In this study, we propose a system that detects negative emotions at each turn in a conversation by leveraging both linguistic and acoustic features. The approach is evaluated on real-world data, with a particular focus on identifying and responding to negative emotion in customer support scenarios. Designed for real-time application, the system is suitable for live deployment in call center environments. Furthermore, we propose an effective prompting strategy for using large language models (LLMs) as annotators, generating labeled data used to fine-tune small language models that achieve performance on par with the LLM used for annotation, while remaining suitable for real-time deployment.
%U https://aclanthology.org/2026.wassa-1.19/
%P 252-264
Markdown (Informal)
[Real-Time Mitigation of Negative Emotion in Customer Care Calls](https://aclanthology.org/2026.wassa-1.19/) (Gangopadhyay & Mehrabani, WASSA 2026)
ACL
- Surupendu Gangopadhyay and Mahnoosh Mehrabani. 2026. Real-Time Mitigation of Negative Emotion in Customer Care Calls. In The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026), pages 252–264, Rabat, Morocco. Association for Computational Linguistics.