Abstract
We exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it’s usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.- Anthology ID:
- W16-4304
- Volume:
- Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Malvina Nissim, Viviana Patti, Barbara Plank
- Venue:
- PEOPLES
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 30–39
- Language:
- URL:
- https://aclanthology.org/W16-4304
- DOI:
- Cite (ACL):
- Chris Pool and Malvina Nissim. 2016. Distant supervision for emotion detection using Facebook reactions. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 30–39, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Distant supervision for emotion detection using Facebook reactions (Pool & Nissim, PEOPLES 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W16-4304.pdf
- Data
- ISEAR