Distant supervision for emotion detection using Facebook reactions

Chris Pool, Malvina Nissim


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
Venues:
PEOPLES | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
30–39
Language:
URL:
https://www.aclweb.org/anthology/W16-4304
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4304.pdf