Abstract
We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation together with non-lexical features. The model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research. The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy. Our code and training data are publicly available.- Anthology ID:
- 2021.ranlp-1.93
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 814–823
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.93
- DOI:
- Cite (ACL):
- Viktorija Leonova and Janis Zuters. 2021. Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 814–823, Held Online. INCOMA Ltd..
- Cite (Informal):
- Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression (Leonova & Zuters, RANLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.ranlp-1.93.pdf