Abstract
In this paper, we present an experiment to detect emotions in tweets. Unlike much previous research, we draw the important distinction between the tasks of emotion detection in a closed world assumption (i.e. every tweet is emotional) and the complicated task of identifying emotional versus non-emotional tweets. Given an apparent lack of appropriately annotated data, we created two corpora for these tasks. We describe two systems, one symbolic and one based on machine learning, which we evaluated on our datasets. Our evaluation shows that a machine learning classifier performs best on emotion detection, while a symbolic approach is better for identifying relevant (i.e. emotional) tweets.- Anthology ID:
- L16-1624
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 3953–3958
- Language:
- URL:
- https://aclanthology.org/L16-1624
- DOI:
- Cite (ACL):
- Luca Dini and André Bittar. 2016. Emotion Analysis on Twitter: The Hidden Challenge. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3953–3958, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Emotion Analysis on Twitter: The Hidden Challenge (Dini & Bittar, LREC 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/L16-1624.pdf