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A Multitask Framework to Detect Depression, Sentiment and Multi-label Emotion from Suicide Notes

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Abstract

The significant rise in suicides is a major cause of concern in public health domain. Depression plays a major role in increasing suicide ideation among the individuals. Although most of the suicides can be avoided with prompt intercession and early diagnosis, it has been a serious challenge to detect the at-risk individuals. Our current work focuses on learning three closely related tasks, viz. depression detection, sentiment citation, and to investigate their impact in analysing the mental state of the victims. We extend the existing standard emotion annotated corpus of suicide notes in English, CEASE, with additional 2539 sentences collected from 120 new notes. We annotate the consolidated corpus with appropriate depression labels and multi-label emotion classes. We further leverage weak supervision to annotate the corpus with sentiment labels. We propose a deep multitask framework that features a knowledge module that uses SenticNet’s IsaCore and AffectiveSpace vector-spaces to infuse external knowledge specific features into the learning process. The system models emotion recognition (the primary task), depression detection and sentiment classification (the secondary tasks) simultaneously. Experiments show that our proposed multitask system obtains the highest cross-validation MR of 56.47 %. Evaluation results show that all our multitask models perform better than their single-task variants indicating that the secondary tasks (depression detection and sentiment classification) improve the performance of the primary task (emotion recognition) when all tasks are learned jointly.

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Notes

  1. https://save.org/about-suicide/suicide-facts/

  2. https://www.apa.org/monitor/2019/03/trends-suicide

  3. https://afsp.org/suicide-statistics/

  4. https://www.who.int/news-room/fact-sheets/detail/suicide

  5. https://www.economist.com/graphic-detail/2020/01/30/americas-suicide-rate-has-increased-for-13-years-in-a-row

  6. https://chapterland.org/wp-content/uploads/sites/13/2017/11/US_FactsFigures_Flyer.pdf

  7. https://www.who.int/news-room/fact-sheets/detail/depression

  8. https://emedicine.medscape.com/article/805459-overview

  9. Resource available at: http://www.iitp.ac.in/~ai-nlp-ml/resources.html

  10. Resource to be made available at: https://www.iitp.ac.in/~ai-nlp-ml/resources.html

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

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

  13. mood and suicidal thought tracking, safety plan development, the recommendation of activities to deter suicidal thoughts, information and education, access to support networks, and access to emergency counselling

  14. ’suicide notes’, ’suicide note \(+ <\mathrm {YEAR}>\)’, ’social media \(+\) suicide note’, etc.

  15. List of some sources of collected notes:

    https://www.denverpost.com/2007/12/07/mall-gunmans-suicide-note-i-just-snapped/

    https://www.huffpost.com/entry/bill-zeller-dead-princeto_n_805689

    https://twitter.com/harrisongolden/status/880816422145970177

    https://www.mirror.co.uk/sport/football/news/gary-speeds-widow-speaks-suicide-13256742

    http://www.homelandnewsng.com/other-news/4518-18-year-old-girl-kills-self-over-man

    https://www.writechoice.co.in/writechoice/geetika-sharma-suicide-note/

    https://en.wikipedia.org/wiki/File:Snead-Ocey_note.jpg

    https://www.storypick.com/doctor-suicide-gay-husband-dowry/

  16. https://ocr.space/

  17. https://www.nltk.org/api/nltk.tokenize.html

  18. Some https://www.nimh.nih.gov/health/publications/depression/index.shtml

  19. Full list of countries: Australia, Canada, China, England, India, Ireland, Japan, Pakistan, Philippines, South Korea, USA and Zimbabwe

  20. https://sentic.net/downloads/

  21. GloVe: http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip

  22. a high-level neural networks API: https://keras.io/

  23. not necessarily same 100 words are considered for all 3 spaces, as vectors are fetched as on availability in the respective vector spaces

  24. https://keras.io/

  25. https://scikit-learn.org/stable/

  26. using loss_weights parameter of keras compile function

  27. We perform Student’s t test for assessing the statistical significance

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Acknowledgements

The authors gratefully acknowledge the support from the project titled ’Development of CDAC Digital Forensic Centre with AI based Knowledge Support Tools’, supported by Ministry of Electronics and Information Technology (MeitY), Government of India and Government of Bihar (project number P-264). The authors would also like to thank Suman Sekhar and Saroj Jha for their valuable efforts in labelling the sentences. Asif Ekbal acknowledges the Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia.

Funding

This study was funded as part of the project titled ’Development of CDAC Digital Forensic Centre with AI based Knowledge Support Tools’ supported by Ministry of Electronics and Information Technology (MeitY), Government of India and Government of Bihar (project number P-264).

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  1. Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Patna, India

    Soumitra Ghosh, Asif Ekbal & Pushpak Bhattacharyya

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Ghosh, S., Ekbal, A. & Bhattacharyya, P. A Multitask Framework to Detect Depression, Sentiment and Multi-label Emotion from Suicide Notes. Cogn Comput 14, 110–129 (2022). https://doi.org/10.1007/s12559-021-09828-7

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