@inproceedings{sawhney-etal-2018-exploring,
title = "Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning",
author = "Sawhney, Ramit and
Manchanda, Prachi and
Mathur, Puneet and
Shah, Rajiv and
Singh, Raj",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6223",
doi = "10.18653/v1/W18-6223",
pages = "167--175",
abstract = "The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention. However, the complexity of the natural language constructs makes this task very challenging. Deep Learning architectures such as LSTMs, CNNs, and RNNs show promise in sentence level classification problems. This work investigates the ability of deep learning architectures to build an accurate and robust model for suicidal ideation detection and compares their performance with standard baselines in text classification problems. The experimental results reveal the merit in C-LSTM based models as compared to other deep learning and machine learning based classification models for suicidal ideation detection.",
}
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%0 Conference Proceedings
%T Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning
%A Sawhney, Ramit
%A Manchanda, Prachi
%A Mathur, Puneet
%A Shah, Rajiv
%A Singh, Raj
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F sawhney-etal-2018-exploring
%X The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention. However, the complexity of the natural language constructs makes this task very challenging. Deep Learning architectures such as LSTMs, CNNs, and RNNs show promise in sentence level classification problems. This work investigates the ability of deep learning architectures to build an accurate and robust model for suicidal ideation detection and compares their performance with standard baselines in text classification problems. The experimental results reveal the merit in C-LSTM based models as compared to other deep learning and machine learning based classification models for suicidal ideation detection.
%R 10.18653/v1/W18-6223
%U https://aclanthology.org/W18-6223
%U https://doi.org/10.18653/v1/W18-6223
%P 167-175
Markdown (Informal)
[Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning](https://aclanthology.org/W18-6223) (Sawhney et al., WASSA 2018)
ACL