EnsyNet: A Dataset for Encouragement and Sympathy Detection

Tiberiu Sosea, Cornelia Caragea


Abstract
More and more people turn to Online Health Communities to seek social support during their illnesses. By interacting with peers with similar medical conditions, users feel emotionally and socially supported, which in turn leads to better adherence to therapy. Current studies in Online Health Communities focus only on the presence or absence of emotional support, while the available datasets are scarce or limited in terms of size. To enable development on emotional support detection, we introduce EnsyNet, a dataset of 6,500 sentences annotated with two types of support: encouragement and sympathy. We train BERT-based classifiers on this dataset, and apply our best BERT model in two large scale experiments. The results of these experiments show that receiving encouragements or sympathy improves users’ emotional state, while the lack of emotional support negatively impacts patients’ emotional state.
Anthology ID:
2022.lrec-1.583
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5444–5449
Language:
URL:
https://aclanthology.org/2022.lrec-1.583
DOI:
Bibkey:
Cite (ACL):
Tiberiu Sosea and Cornelia Caragea. 2022. EnsyNet: A Dataset for Encouragement and Sympathy Detection. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5444–5449, Marseille, France. European Language Resources Association.
Cite (Informal):
EnsyNet: A Dataset for Encouragement and Sympathy Detection (Sosea & Caragea, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.583.pdf
Code
 tsosea2/ensynet