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A Review of LIWC and Machine Learning Approaches On Mental Health Diagnosis

Year 2023, Volume: 1 Issue: 2, 71 - 92, 25.10.2023

Bahar Sert Selami Varol Ülker

Abstract

Machine learning methods are becoming increasingly popular data analysis and enable learning from data in many different fields. In the field of mental healthcare, these methods provide support to mental health professionals in various ways. The diagnosis of mental disorders is one of these areas where machine learning methods can be of assistance. Firstly, Pennebaker and his colleagues developed a computer program for dictionary-based automatic quantitative text analysis which detects many mental disorder diagnosis and symptoms such as depression, schizophrenia and suicidal tendencies through text analysis. In this study, Machine learning and Linguistic Inquiry Word Count (LIWC) studies conducted in the field of mental disorder diagnosis were examined. Researchers aim to integrate LIWC with machine learning to conduct more comprehensive studies. The objective of this study is to examine how combining Machine learning and LIWC methods can detect mental disorder with a focus on comparative research. For this purpose, publications related to machine learning and LIWC in Google Scholar, Web of Science, Scopus, EBSCO, PubMed were examined. Studies utilizing machine learning and LIWC methods in mental health diagnosis were reviewed to establish an overview of the general state of the literature. A comprehensive table summarizing 15 articles examining the impact of integrating machine learning and LIWC on mental disorder identification was compiled. Subsequently, the working principles of machine learning and LIWC were examined and research conducted in the field of mental disorder diagnosis was reviewed. Furthermore, some studies about mental disorder diagnosis were set out in table. Further research particularly those integrating or comparing these two methods needed to better understand machine learning and Linguistic Inquiry Word Count in mental disorder detection.

Keywords

LIWC Machine Learning Mental Disorders Psychology Text analysis

References

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Year 2023, Volume: 1 Issue: 2, 71 - 92, 25.10.2023

Bahar Sert Selami Varol Ülker

Abstract

References

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  • Glauser, T., Santel, D., DelBello, M., Faist, R., Toon, T., Clark, P., ... & Pestian, J. (2020). “Identifying Epilepsy Psychiatric Comorbidities with Machine Learning”, Acta Neurologica Scandinavica, 141(5): 388-396. DOI: 10.1111/ane.13216.
  • Grijalva, E., Newman, D. A., Tay, L., Donnellan, M. B., Harms, P. D., Robins, R. W., & Yan, T. (2015). “Gender Differences in Narcissism: A Meta-analytic Review”, Psychological Bulletin, 141(2): 261–310. DOI: 10.1037/a0038231.
  • Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L., ... & Merchant, R. (2019). “Studying Expressions of Loneliness in Individuals Using Twitter: An Observational Study”, BMJ open, 9(11), e030355. DOI: 10.1136/bmjopen-2019- 030355
  • He, Lang, Cui Cao (2018). “Automated Depression Analysis Using Convolutional Neural Networks from Speech”, Journal of Biomedical Informatics, 83:103–111. DOI: 10.1016/j.jbi.2018.05.007.
  • Huang, Jiaji, Qiang Qiu, Kenneth Church (2019). “Hubless Nearest Neighbor Search for Bilingual Lexicon Induction”, In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4072-4080. DOI: 10.18653/v1/P19-1399.
  • Huang, Yan-Jia, Yi-Tin Lin, Chen-Chung Liu, Lue-En Lee, Shu-Hui Hung, Jun-Kai Lo, and Li-Chen Fu (2022). “Assessing Schizophrenia Patients through Linguistic and Acoustic Features Using Deep Learning Techniques”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30:947-956. DOI: 10.1109/TNSRE.2022.3163777.
  • Islam, Md Rafiqul, Muhammad Ashad Kabir, Ashir Ahmed, Abu Raihan M. Kamal, Hua Wang, Anwaar Ulhaq (2018). “Depression Detection from Social Network Data Using Machine Learning Techniques”, Health Information Science and Systems, 6(1), 8. DOI: 10.1007/s13755-018-0046-0.
  • Jordan, Michael I. and Tom M. Mitchell (2015). “Machine Learning: Trends, Perspectives, and Prospects”. Science, 349(6245): 255–260. DOI: 10.1126/science.aaa8415.
  • Kaur, Harleen, Shafqat U. Ahsaan, Bhavya Alankar and Victor Chang (2021). “A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets”, Information Systems Frontiers, 23(6):1417–1429. DOI: 10.1007/s10796-021-10135-7.
  • Kelley, Sean W., Caoimhe N. Mhaonaigh, Louise Burke. et al. (2022). “Machine Learning of Language Use on Twitter Reveals Weak and Non-specific Predictions”, npj Digital Medicine, 5(35). DOI: 10.1038/s41746-022-00576-y.
  • Lee, Chris, Tess V. Zanden, Emiel Krahmer, Maria Mos, and Alexander Schouten (2019). “Automatic Identification Of Writers’ Intentions: Comparing Different Methods For Predicting Relationship Goals In Online Dating Profile Texts”, Proceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-Generated Text, 94-100, DOI: 10.18653/v1/d19-5512.
  • Liu, Yali and Louisa Buckingham (2022). “Language Choice and Academic Publishing: A Social-ecological Perspective on Languages other than English”, Journal of Multilingual and Multicultural Development, Advance online publication DOI: 10.1080/01434632.2022.2080834.
  • Lyu, Sihua, Ren Xiaopeng, Du Yihua, and Nan Zhao. (2023). “Detecting Depression of Chinese Microblog Users Via Text Analysis: Combining Linguistic Inquiry Word Count (LIWC) with Culture and Suicide Related Lexicons”, Frontiers in Psychiatry, 14:1121583, DOI: 10.3389/fpsyt.2023.1121583.
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There are 56 citations in total.

Details

Primary Language Turkish
Subjects Sociology (Other)
Journal Section Articles
Authors

Bahar Sert 0009-0003-3806-9791

Selami Varol Ülker 0000-0002-6385-6418

Publication Date October 25, 2023
Published in Issue Year 2023 Volume: 1 Issue: 2

Cite

APA Sert, B., & Ülker, S. V. (2023). A Review of LIWC and Machine Learning Approaches On Mental Health Diagnosis. Social Review of Technology and Change, 1(2), 71-92.
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