Identifying Empathetic Messages in Online Health Communities

Hamed Khanpour, Cornelia Caragea, Prakhar Biyani


Abstract
Empathy captures one’s ability to correlate with and understand others’ emotional states and experiences. Messages with empathetic content are considered as one of the main advantages for joining online health communities due to their potential to improve people’s moods. Unfortunately, to this date, no computational studies exist that automatically identify empathetic messages in online health communities. We propose a combination of Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks, and show that the proposed model outperforms each individual model (CNN and LSTM) as well as several baselines.
Anthology ID:
I17-2042
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
246–251
Language:
URL:
https://www.aclweb.org/anthology/I17-2042
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-2042.pdf