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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.wnut-1.12.pdf
- Code
- hadifar/dutchemotiondetection + additional community code