Sentiment-Aware Dialogue Flow Discovery for Interpreting Communication Trends

Patrícia Ferreira, Isabel Carvalho, Ana Alves, Catarina Silva, Hugo Gonçalo Oliveira


Abstract
Customer-support services increasingly rely on automation, whether fully or with human intervention. Despite optimising resources, this may result in mechanical protocols and lack of human interaction, thus reducing customer loyalty. Our goal is to enhance interpretability and provide guidance in communication through novel tools for easier analysis of message trends and sentiment variations. Monitoring these contributes to more informed decision-making, enabling proactive mitigation of potential issues, such as protocol deviations or customer dissatisfaction. We propose a generic approach for dialogue flow discovery that leverages clustering techniques to identify dialogue states, represented by related utterances. State transitions are further analyzed to detect prevailing sentiments. Hence, we discover sentiment-aware dialogue flows that offer an interpretability layer to artificial agents, even those based on black-boxes, ultimately increasing trustworthiness. Experimental results demonstrate the effectiveness of our approach across different dialogue datasets, covering both human-human and human-machine exchanges, applicable in task-oriented contexts but also to social media, highlighting its potential impact across various customer-support settings.
Anthology ID:
2024.sigdial-1.24
Volume:
Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2024
Address:
Kyoto, Japan
Editors:
Tatsuya Kawahara, Vera Demberg, Stefan Ultes, Koji Inoue, Shikib Mehri, David Howcroft, Kazunori Komatani
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
274–288
Language:
URL:
https://aclanthology.org/2024.sigdial-1.24
DOI:
10.18653/v1/2024.sigdial-1.24
Bibkey:
Cite (ACL):
Patrícia Ferreira, Isabel Carvalho, Ana Alves, Catarina Silva, and Hugo Gonçalo Oliveira. 2024. Sentiment-Aware Dialogue Flow Discovery for Interpreting Communication Trends. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 274–288, Kyoto, Japan. Association for Computational Linguistics.
Cite (Informal):
Sentiment-Aware Dialogue Flow Discovery for Interpreting Communication Trends (Ferreira et al., SIGDIAL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.sigdial-1.24.pdf