Mental Health Analysis using Natural Language Processing

Authors

  • Bindu V S  MTECH CSE Department, NHCE, Bangalore, India
  • Susmita N Gonkar  MTECH CSE Department, NHCE, Bangalore, India

Keywords:

Natural Language Processing; Machine Learning, Mental Health, Whitespace; Psychology

Abstract

Our increasingly digital life provides a wealth of data about our behavior, beliefs, mood, and well-being. This data provides some insight into the lives of patients outside the healthcare setting, and in aggregate can be insightful for the person’s mental health and emotional crisis. Here, we introduce this community to some of the recent advancement in using natural language processing and machine learning to provide insight into mental health of both individuals and populations. We advocate using these linguistic signals as a supplement to those that are collected in the health care system, ?lling in some of the so-called “whitespace” between visits. Whitespace information provides a lens through which we can analyze psychological phenomena like emotional crisis, suicide attempts, and drug relapse.

References

  1. Glen Coppersmith1, Casey Hilland1, Ophir Frieder2, Ryan Leary1, “Scalable Mental Health Analysis in the Clinical Whitespace via Natural Language Processing” 2017
  2. SAMHSA, “Substance abuse and mental health services administration,” in Results from the 2013 National Survey on Drug Use and Health: Mental Health Findings, NSDUH Series H-49, HHS Publication No. (SMA) 14-4887, Rockville, MD, 2014.
  3. John pestian ”Suicide note classification using Natural Language Processing: A Content Analysis”2011
  4. S. C. Curtin, M. Warner, and H. Hedegaard, “Increase in suicide in the united states, 1999-2014.” NCHS data brief, no. 241, pp. 1–8, 2016.
  5. Glen Coppersmith1, Casey Hilland1, Ophir Frieder2, Ryan Leary1, “Scalable Mental Health Analysis in the Clinical Whitespace via Natural Language Processing” 2017
  6. SAMHSA, “Substance abuse and mental health services administration,” in Results from the 2013 National Survey on Drug Use and Health: Mental Health Findings, NSDUH Series H-49, HHS Publication No. (SMA) 14-4887, Rockville, MD, 2014.
  7. John pestian ”Suicide note classification using Natural Language Processing: A Content Analysis”2011
  8. S. C. Curtin, M. Warner, and H. Hedegaard, “Increase in suicide in the united states, 1999-2014.” NCHS data brief, no. 241, pp. 1–8, 2016.
  9. J. W. Pennebaker, C. K. Chung, M. Ireland, A. Gonzales, and R. J. Booth, The development and psychometric properties of LIWC2007. Austin, TX: LIWC.net, 2007.
  10. C. Chung and J. Pennebaker, “The psychological functions of function words,” Social Communication, 2007.
  11. M. De Choudhury, M. Gamon, S. Counts, and E. Horvitz, “Predicting depression via social media,” in Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM), 2013.
  12.  G. Coppersmith, M. Dredze, C. Harman, and K. Hollingshead, “From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses,” in Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Denver, Colorado, USA: North American Chapter of the Association for Computational Linguistics, June 2015.

Downloads

Published

2019-12-30

Issue

Section

Research Articles

How to Cite

[1]
Bindu V S, Susmita N Gonkar, " Mental Health Analysis using Natural Language Processing" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 9, pp.397-400, November-December-2019.