Identifying Emotional Support in Online Health Communities

Hamed Khanpour, Cornelia Caragea, Prakhar Biyani

AAAI 2018 pp. 8099-8100

doi:10.1609/AAAI.V32I1.12170 /aaai/2018/khanpour2018aaai-identifying/

Abstract

Extracting emotional support in Online Health Communities provides insightful information about patients’ emotional states. Current computational approaches to identifying emotional messages, i.e., messages that contain emotional support, are typically based on a set of handcrafted features. In this paper, we show that high-level and abstract features derived from a combination of convolutional neural networks (CNN) with Long Short Term Memory (LSTM) networks can be successfully employed for emotional message identification and can obviate the need for handcrafted features.

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Cite

Text

Khanpour et al. "Identifying Emotional Support in Online Health Communities." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12170

Markdown

[Khanpour et al. "Identifying Emotional Support in Online Health Communities." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/khanpour2018aaai-identifying/) doi:10.1609/AAAI.V32I1.12170

BibTeX

@inproceedings{khanpour2018aaai-identifying,
  title     = {{Identifying Emotional Support in Online Health Communities}},
  author    = {Khanpour, Hamed and Caragea, Cornelia and Biyani, Prakhar},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8099-8100},
  doi       = {10.1609/AAAI.V32I1.12170},
  url       = {https://mlanthology.org/aaai/2018/khanpour2018aaai-identifying/}
}