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        <title>CEASE, a Corpus of Emotion Annotated Suicide notes in English</title>
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    <name type="personal">
        <namePart type="given">Soumitra</namePart>
        <namePart type="family">Ghosh</namePart>
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    <name type="personal">
        <namePart type="given">Asif</namePart>
        <namePart type="family">Ekbal</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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        <dateIssued>2020-may</dateIssued>
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            <title>Proceedings of the 12th Language Resources and Evaluation Conference</title>
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            <publisher>European Language Resources Association</publisher>
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    <abstract>A suicide note is usually written shortly before the suicide and it provides a chance to comprehend the self-destructive state of mind of the deceased. From a psychological point of view, suicide notes have been utilized for recognizing the motive behind the suicide. To the best of our knowledge, there is no openly accessible suicide note corpus at present, making it challenging for the researchers and developers to deep dive into the area of mental health assessment and suicide prevention. In this paper, we create a fine-grained emotion annotated corpus (CEASE) of suicide notes in English and develop various deep learning models to perform emotion detection on the curated dataset. The corpus consists of 2393 sentences from around 205 suicide notes collected from various sources. Each sentence is annotated with a particular emotion class from a set of 15 fine-grained emotion labels, namely (forgiveness, happiness_peacefulness, love, pride, hopefulness, thankfulness, blame, anger, fear, abuse, sorrow, hopelessness, guilt, information, instructions). For the evaluation, we develop an ensemble architecture, where the base models correspond to three supervised deep learning models, namely Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). We obtain the highest test accuracy of 60.17% and cross-validation accuracy of 60.32%</abstract>
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        <url>https://aclanthology.org/2020.lrec-1.201</url>
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        <date>2020-may</date>
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            <start>1618</start>
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%0 Conference Proceedings
%T CEASE, a Corpus of Emotion Annotated Suicide notes in English
%A Ghosh, Soumitra
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F ghosh-etal-2020-cease
%X A suicide note is usually written shortly before the suicide and it provides a chance to comprehend the self-destructive state of mind of the deceased. From a psychological point of view, suicide notes have been utilized for recognizing the motive behind the suicide. To the best of our knowledge, there is no openly accessible suicide note corpus at present, making it challenging for the researchers and developers to deep dive into the area of mental health assessment and suicide prevention. In this paper, we create a fine-grained emotion annotated corpus (CEASE) of suicide notes in English and develop various deep learning models to perform emotion detection on the curated dataset. The corpus consists of 2393 sentences from around 205 suicide notes collected from various sources. Each sentence is annotated with a particular emotion class from a set of 15 fine-grained emotion labels, namely (forgiveness, happiness_peacefulness, love, pride, hopefulness, thankfulness, blame, anger, fear, abuse, sorrow, hopelessness, guilt, information, instructions). For the evaluation, we develop an ensemble architecture, where the base models correspond to three supervised deep learning models, namely Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). We obtain the highest test accuracy of 60.17% and cross-validation accuracy of 60.32%
%U https://aclanthology.org/2020.lrec-1.201
%P 1618-1626

Markdown (Informal)

[CEASE, a Corpus of Emotion Annotated Suicide notes in English](https://aclanthology.org/2020.lrec-1.201) (Ghosh et al., LREC 2020)

• CEASE, a Corpus of Emotion Annotated Suicide notes in English (Ghosh et al., LREC 2020)

ACL

• Soumitra Ghosh, Asif Ekbal, and Pushpak Bhattacharyya. 2020. CEASE, a Corpus of Emotion Annotated Suicide notes in English. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1618–1626, Marseille, France. European Language Resources Association.