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2022 | OriginalPaper | Chapter

CARES: CAuse Recognition for Emotion in Suicide Notes

Authors : Soumitra Ghosh, Swarup Roy, Asif Ekbal, Pushpak Bhattacharyya

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

Inspired by recent advances in emotion-cause extraction in texts and its potential in research on computational studies in suicide motives and tendencies and mental health, we address the problem of cause identification and cause extraction for emotion in suicide notes. We introduce an emotion-cause annotated suicide corpus of 5769 sentences by labeling the benchmark CEASE-v2.0 dataset (4932 sentences) with causal spans for existing annotated emotions. Furthermore, we expand the utility of the existing dataset by adding emotion and emotion cause annotations for an additional 837 sentences collected from 67 non-English suicide notes (Hindi, Bangla, Telugu). Our proposed approaches to emotion-cause identification and extraction are based on pre-trained transformer-based models that attain performance figures of 83.20% accuracy and 0.76 Ratcliff-Obershelp similarity, respectively. The findings suggest that existing computational methods can be adapted to address these challenging tasks, opening up new research areas.

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https://www.who.int/news/item/17-06-2021-one-in-100-deaths-is-by-suicide.

https://www.befrienders.org/suicide-statistics.

http://news.sina.com.cn/society/.

forgiveness, happiness_peacefulness, love, pride, hopefulness, thankfulness, blame, anger, fear, abuse, sorrow, hopelessness, guilt, information, instructions.

Dataset available at https://www.iitp.ac.in/ ai-nlp-ml/resources.html#CARES.

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Title: CARES: CAuse Recognition for Emotion in Suicide Notes
Authors: Soumitra Ghosh
Swarup Roy
Asif Ekbal
Pushpak Bhattacharyya
Publisher: Springer International Publishing
Book: Advances in Information Retrieval
Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-99739-7_15

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