@inproceedings{khanpour-etal-2017-identifying,
title = "Identifying Empathetic Messages in Online Health Communities",
author = "Khanpour, Hamed and
Caragea, Cornelia and
Biyani, Prakhar",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2042",
pages = "246--251",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Identifying Empathetic Messages in Online Health Communities
%A Khanpour, Hamed
%A Caragea, Cornelia
%A Biyani, Prakhar
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F khanpour-etal-2017-identifying
%X 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.
%U https://aclanthology.org/I17-2042
%P 246-251
Markdown (Informal)
[Identifying Empathetic Messages in Online Health Communities](https://aclanthology.org/I17-2042) (Khanpour et al., IJCNLP 2017)
ACL
- Hamed Khanpour, Cornelia Caragea, and Prakhar Biyani. 2017. Identifying Empathetic Messages in Online Health Communities. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 246–251, Taipei, Taiwan. Asian Federation of Natural Language Processing.