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

Öz

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.

Anahtar Kelimeler

• LIWC
• Machine Learning
• Mental Disorders
• Psychology
• Text analysis

Kaynakça

1. Aghazadeh, Sanaz, Kris Hoang, and Bradley Pomeroy (2022). “Using LIWC to Analyze Participants' Psychological Processing in Accounting JDM Research”, AUDITING: A Journal of Practice & Theory, 41(3): 1–20. DOI: 10.2308/AJPT-2020-060.
2. Bae, Yi Ji, Midan Shim, and Won Hee Lee (2021). “Schizophrenia Detection Using Machine Learning Approach from Social Media Content”, Sensors, 21(17): 5924. DOI: 10.3390/s21175924.
3. Bartal, Alon, Kathleen M. Jagodnik, Sabrina J. Chan, Mrithula S. Babu and Sharon Dekel (2022). “Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives”, medRxiv : the preprint server for health sciences. DOI: 10.1101/2022.08.30.22279394.
4. Bartal, A., Kathleen M. Jagodnik, Sabrina J. Chan, Mirithula S. Babu, & Sharon Dekel (2023). “Identifying Women with Postdelivery Posttraumatic Stress Disorder Using Natural Language Processing Of Personal Childbirth Narratives”, American Journal of Obstetrics & Gynecology MFM, 5(3), 100834. DOI: 10.1016/j.ajogmf.2022.100834.
5. Biggiogera, Jacopo., George Boateng, Peter Hilpert, Matthew Vowels, Guy Bodenmann, Mona Neysari, ... & Tobias Kowatsch (2021). “BERT meets LIWC: Exploring State-of-the-art Language Models for Predicting Communication Behavior in Couples’ Conflict Interactions. In Companion Publication of the 2021 International Conference on Multimodal Interaction, 385-389. DOI: 10.1145/3461615.3485423
6. Bi̇li̇k, M. Zuhal, Eylül Ceren Hekimoğlu and Faruk Gençöz (2021). “Traces of the Unconscious in Language”, Language and Psychoanalysis, 10(1): 27-35. DOI: 10. 1-9. 10.7565/landp.v10i1.4390.
7. Binjie, Gu, Weili Xiong, Zhonghu Bai (2020). “Human Action Recognition Based on Supervised Class-specific Dictionary Learning with Deep Convolutional Neural Network Features”, Computers, Materials & Continua, 62(3): 243-262. DOI: 10.32604/cmc.2020.06898.
8. Birnbaum, M.L., S. K. Ernala, A. F. Rizvi, E. Arenare, A. R. Van Meter, M De Choudhury, J. M. Kane (2019). “Detecting Relapse in Youth with Psychotic Disorders Utilizing Patient-generated and Patient-contributed Digital Data from Facebook”, npj Schizophrenia 5(17). DOI: 10.1038/s41537-019-0085-9.

9. Birnbaum, M.L., Raquel Norel., Anna Van Meter, Asra, F. Ali, Elizabeth Arenare, Elif Eyigöz, Carla Agurto, Nicole Germano, John, M. Kane, Guillermo A. Cecchi (2020). “Identifying Signals Associated with Psychiatric Illness Utilizing Language and Images Posted to Facebook”, npj Schizophrenia, 6(38). DOI: 10.1038/s41537-020-00125-0.
10. Boyd, Ryan L. and H. Andrew Schwarts (2021). “Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field”, Journal of Language and Social Psychology, 40(1): 21–41. DOI: 10.1177/0261927X20967028.
11. Burkhardt, Hannah A., George S. Alexopoulos, Michael D. Pullmann, Thomas D. Hull, Patricia A. Areán, and Trevor Cohen (2021). “Behavioral Activation and Depression Symptomatology: Longitudinal Assessment Of Linguistic Indicators In Text-based Therapy Sessions”, Journal of Medical Internet Research, 23(7). DOI: 10.2196/28244.
12. Cheng, Qijin., Tim Mh Li., Chi- Leung Kwok, Tinhshao Zhu, & Paul Sf Yip. (2017). “Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study”, Journal of Medical Internet Research, 19(7), e243. DOI: 10.2196/jmir.7276
13. Chowdhury, Ekram Ahmed, Guy Meno-Tetang, Hsueh Yuan Chang, Shengjia Wu, Hsien Wei Huang, Tanguy Jamier, Jayanth Chandran, and Dhaval K. Shah (2021). “Current Progress and limitations of AAV Mediated Delivery of Protein Therapeutic Genes and the Importance of Developing Quantitative Pharmacokinetic/Pharmacodynamic (PK/PD) Models”, Advanced Drug Delivery Reviews, 170: 214–237. DOI: 10.1016/j.addr.2021.01.017.
14. Cutler, Andrew D., Stephen W. Carden, Hannah L. Dorough, & Nicholas S. Holtzman. (2021). “Inferring Grandiose Narcissism from Text: LIWC versus Machine Learning”, Journal of Language and Social Psychology, 40(2): 260-276. DOI: 10.1177/0261927X20936309.
15. Eichstaedt, Johannes C., Margaret L. Kern, David B. Yaden, H. A. Schwartz, S. Giorgi, Gregory Park, ... and Lyle H. Ungar (2021). “Closed-and Open-Vocabulary Approaches to Text Analysis: A Review, Quantitative Comparison, and Recommendations”, Psychological Methods, 26(4): 398. DOI: 10.1037/met0000349.
16. Enevoldsen, Kenneth C., Andreas A. Danielsen, Christopher Rohde, Oskar H. Jefsen, Kristoffer L. Nielbo, & Søren D. Østergaard. (2022). “Monitoring of COVID-19 Pandemic-related Psychopathology Using Machine Learning”, Acta Neuropsychiatrica, 34(3): 148-152. DOI: 10.1017/neu.2022.2.
17. Fatima, I., Abbasi, B. U. D., Khan, S., Al‐Saeed, M., Ahmad, H. F., & Mumtaz, R. (2019). “Prediction of Postpartum Depression Using Machine Learning Techniques from Social Media Text”, Expert Systems, 36(4), e12409. DOI: 10.1111/exsy.12409.
18. Gaston, J., Narayanan, M., Dozier, G., Cothran, D. L., Arms-Chavez, C., Rossi, M., ... & Xu, J. (2018, November). “Authorship Attribution via Evolutionary Hybridization of Sentiment Analysis, LIWC, and Topic Modeling Features”, In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 933-940. DOI: 10.1109/SSCI.2018.8628647.
19. 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.
20. 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.
21. 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
22. 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.
23. 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.
24. 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.
25. 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.
26. Jordan, Michael I. and Tom M. Mitchell (2015). “Machine Learning: Trends, Perspectives, and Prospects”. Science, 349(6245): 255–260. DOI: 10.1126/science.aaa8415.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. Marengo, D., D. Azucar, F. Giannotta, V. Basile, M. Settanni (2019). “Exploring the Association between Problem Drinking and Language Use on Facebook in Young Adults”, Heliyon, 5(10), e02523. DOI: 10.1016/j.heliyon.2019.e02523
33. Massell, Johannes, Roselind Lieb, Andrea Meyer, and Eric Mayor (2022). “Fluctuations of Psychological States on Twitter Before and During COVID-19”, PloS ONE, 17(12). DOI: 10.1371/journal.pone.0278018.
34. Monzani, Dario, Alessandra Gorini, Davide Mazzoni, and Gabriella Pravettoni (2021). “Brief report – ‘Every little thing gonna be all right’ (at least for me): Dispositional Optimists Display Higher Optimistic Bias for Infection During the Italian COVID-19 Outbreak”, Personality and Individual Differences, 168:110388. DOI: 10.1016/j.paid.2020.110388.
35. Morales Michelle Renee and Rivka Levitan, “Speech vs. text: A Comparative Analysis of Features for Depression Detection Systems”, IEEE Spoken Language Technology Workshop (SLT), San Diego, CA, USA, 2016,136-143, (2016). DOI: 10.1109/SLT.2016.7846256.
36. Pan, Wei, Xianbin Wang, Wenwei Zhou, Bowen Hang, Liwen Guo (2023). “Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches”, International Journal of Environmental Research and Public Health, 20(3): 2688. DOI: 10.3390/ijerph20032688.
37. Pennebaker, James W. and Cindy K. Chung (2007). “Expressive Writing, Emotional Upheavals, and Health”, H. S. Friedman and R. C. Silver (eds.), Foundations of Health Psychology, 263–284). Oxford University Press.
38. Pestian, John, Daniel Santel, Michael Sorter, Ulya Bayram, Brian Connolly, Tracy Glauser, Melissa DelBello, Suzanne Tamang, Kevin Cohen (2020). “A Machine Learning Approach to Identifying Changes in Suicidal Language”, Suicide and Life-Threatening Behavior, 50(5): 939-947, DOI:10.1111/sltb.126.
39. Ramírez‐Esparza, Nairan, Adrian García‐Sierra, and Patricia K. Kuhl (2014). “Look Who's Talking: Speech Style and Social Context in Language Input to Infants are Linked to Concurrent and Future Speech Development”, Developmental Science, 17(6):880-891.
40. Robinson, Eric, Angelina R. Sutin, Michael Daly, and Andrew Jones (2022). “A Systematic Review and Meta-analysis of Longitudinal Cohort Studies Comparing Mental Health Before versus during the COVID-19 Pandemic in 2020”, Journal of Affective Disorders, 296:567–576. DOI: 10.1016/j.jad.2021.09.098.
41. Safa, Ramin, Peyman Bayat, and Lelia Moghtader (2022). “Automatic Detection of Depression Symptoms in Twitter Using Multimodal Analysis”, The Journal of Supercomputing, 78(4): 4709–4744. DOI: 10.1007/s11227-021-04040-8.
42. Salsabila, Ghina and Erwin Setiawan (2021). “Semantic Approach for Big Five Personality Prediction on Twitter”, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(4): 680-687. DOI: 680-687. 10.29207/resti.v5i4.3197.
43. Shuping, Xing (2018). “Analysis of the Characteristics of Advertising English Language”, 8th International Conference on Social Network, Communication and Education (SNCE 2018). DOI: 10.2991/snce-18.2018.252
44. Stirman, Shannon. W., & James, W. Pennebaker (2001). “Word Use in the Poetry Of Suicidal And Nonsuicidal Poets”, Psychosomatic Medicine, 63(4): 517-522. DOI: 10.1097/00006842-200107000-00001.
45. Su, Yue, Jia Xue, Xiaoqian Liu, Peijing Wu, Junxiang Chen, Chen Chen, Tianli Liu, Weigang Gong, & Tingshao Zhu,. (2020). Examining the Impact of COVID-19 Lockdown in Wuhan and Lombardy: A Psycholinguistic Analysis on Weibo and Twitter. International journal of Environmental Research and Public Health, 17(12): 4552. DOI: 10.3390/ijerph17124552.
46. Sundararajan, Rajeswari, Preetha Menon, & Balaji Jayakrishnan. (2022). “Future of Artificial Intelligence and Machine Learning in Marketing 4.0. In Proceedings of the 7th International Conference on Big Data and Computing, 82-87. DOI: 10.1145/3545801.3545813.
47. Taawab, Al, A., Rahman, M., Islam, Z., Mustari, N., Roy, S., & Alam, M. G. R. Detecting Self-Esteem Level And Depressive İndication Due To Different Parenting Style Using Supervised Learning Techniques. 2022 9th International Conference on Behavioural and Social Computing (BESC). pp. 1-6, (2022). DOI: 10.1109/BESC57393.2022.9995147.
48. Tausczik, Yla R. & James W. Pennebaker (2010). ‘The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods’, Journal of Language and Social Psychology, 29(1): 24-54.
49. Teferra, B. G., & Rose, J. (2023). “Predicting Generalized Anxiety Disorder from Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study”, JMIR Mental Health, 10, e44325. DOI: 10.2196/44325.
50. Thompson, Andrew D., & Maria Hartwig. (2023). “The Language of High‐stakes Truths and Lies: Linguistic Analysis of True and Deceptive Statements Made during Sexual Homicide Interrogations”, Legal and Criminological Psychology, 28(1): 34–44. DOI: 10.1111/lcrp.12214.
51. Ülker, Selami Varol (n.d.). “The Associations between Self-Deception, Depressive Mood, and Attachment Dimensions with Linguistic Inquiry and Word Count”, International Journal of Social Science and Humanities Research, 5(8): 29. DOI: 10.47191/ijsshr/v5-i8-29.
52. Vize, Colin. E., Donald R. Lynam, Katherine L. Collison, & Joshua D. Miller. (2018). “Differences among Dark Triad Components: A Meta-analytic Investigation”, Personality Disorders, 9(2): 101–111. DOI: 10.1037/per0000222.
53. Wang, Yi-Chia, Robert Kraut, and John M. Levine (2012). “To Stay or Leave? The Relationship of Emotional and Informational Support to Commitment in Online Health Support Groups”, CSCW '12 Computer Supported Cooperative Work, Seattle, WA, USA, February 11-15. DOI: 10.1145/2145204.2145329.
54. Weintraub, M. J., Posta, F., Arevian, A. C., & Miklowitz, D. J. (2021). “Using Machine Learning Analyses of Speech to Classify Levels of Expressed Emotion in Parents of Youth with Mood Disorders”, Journal of Psychiatric Research, 136, 39–46. DOI: 10.1016/j.jpsychires.2021.01.019.
55. Wang, Xinyi, Sebastian Ruder, and Graham Neubig (2022). “Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation”, In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),863–877. DOI: 10.18653/v1/2022.acl-long.61.
56. Zhang, Shiyang, Karen L. Fingerman, Kira S. Birditt (2023). “Detecting Narcissism from Older Adults’ Daily Language Use: A Machine Learning Approach”, The journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 78(9): 1493–1500. DOI: 10.1093/geronb/gbad061.