An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues

Sofie Labat, Amir Hadifar, Thomas Demeester, Veronique Hoste


Abstract
The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively. This paper measures the potential of prediction models on that task, based on a real-world dataset of Dutch Twitter conversations in the domain of customer service. We find that modeling emotion trajectories has a small, but measurable benefit compared to predictions based on isolated turns. The models used in our study are shown to generalize well to different companies and economic sectors.
Anthology ID:
2022.wnut-1.12
Volume:
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–112
Language:
URL:
https://aclanthology.org/2022.wnut-1.12
DOI:
Bibkey:
Cite (ACL):
Sofie Labat, Amir Hadifar, Thomas Demeester, and Veronique Hoste. 2022. An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 106–112, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues (Labat et al., WNUT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wnut-1.12.pdf
Code
 hadifar/dutchemotiondetection +  additional community code