@inproceedings{sawhney-etal-2018-computational,
title = "A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets",
author = "Sawhney, Ramit and
Manchanda, Prachi and
Singh, Raj and
Aggarwal, Swati",
editor = "Shwartz, Vered and
Tabassum, Jeniya and
Voigt, Rob and
Che, Wanxiang and
de Marneffe, Marie-Catherine and
Nissim, Malvina",
booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-3013",
doi = "10.18653/v1/P18-3013",
pages = "91--98",
abstract = "Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide. This paper proposes a novel supervised approach for detecting suicidal ideation in content on Twitter. A set of features is proposed for training both linear and ensemble classifiers over a dataset of manually annotated tweets. The performance of the proposed methodology is compared against four baselines that utilize varying approaches to validate its utility. The results are finally summarized by reflecting on the effect of the inclusion of the proposed features one by one for suicidal ideation detection.",
}
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<abstract>Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide. This paper proposes a novel supervised approach for detecting suicidal ideation in content on Twitter. A set of features is proposed for training both linear and ensemble classifiers over a dataset of manually annotated tweets. The performance of the proposed methodology is compared against four baselines that utilize varying approaches to validate its utility. The results are finally summarized by reflecting on the effect of the inclusion of the proposed features one by one for suicidal ideation detection.</abstract>
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%0 Conference Proceedings
%T A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets
%A Sawhney, Ramit
%A Manchanda, Prachi
%A Singh, Raj
%A Aggarwal, Swati
%Y Shwartz, Vered
%Y Tabassum, Jeniya
%Y Voigt, Rob
%Y Che, Wanxiang
%Y de Marneffe, Marie-Catherine
%Y Nissim, Malvina
%S Proceedings of ACL 2018, Student Research Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sawhney-etal-2018-computational
%X Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide. This paper proposes a novel supervised approach for detecting suicidal ideation in content on Twitter. A set of features is proposed for training both linear and ensemble classifiers over a dataset of manually annotated tweets. The performance of the proposed methodology is compared against four baselines that utilize varying approaches to validate its utility. The results are finally summarized by reflecting on the effect of the inclusion of the proposed features one by one for suicidal ideation detection.
%R 10.18653/v1/P18-3013
%U https://aclanthology.org/P18-3013
%U https://doi.org/10.18653/v1/P18-3013
%P 91-98
Markdown (Informal)
[A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets](https://aclanthology.org/P18-3013) (Sawhney et al., ACL 2018)
ACL