Depression Sentiment Analysis using Machine Learning Techniques:A Review
DOI:
https://doi.org/10.22399/ijcesen.851Keywords:
Depression, Social Media, Twitter Blogs, Feature Extraction, Deep Learning, Machine Learning TechniquesAbstract
Depression is one of the habitual psychological well-being diseases and a significant number of depressed individuals end their lives. People suffering from depression don’t ask for help from psychological doctors due to hesitation or unawareness about depression that causes a delay in diagnosis and treatment. A lot of people share their opinions and emotions on social networking sites. Several studies of social networking site posts related to depression rely upon Facebook, Twitter, Blogs, and other social networks because they help in recording behavioral attributes which are related to a person’s thinking, socialization, communication, etc. Datasets from various social networking sites are useful for depression sentiment analysis. Various machine learning and deep learning techniques like Naïve Bayes, maximum entropy, Support Vector Machine (SVM), and Decision Tree classifiers neural networks, deep neural networks, recurrent neural networks etc. have been used for depression detection. This paper presents a review on sentiment analysis performed on social media platforms for detection of depression The datasets utilized are also discussed. A comparative analysis of existing work in the area of depression detection is provided to get a clear understanding of the techniques used. Finally, challenges and future work which can be done in the field of depression detection is also discussed
References
Tong L., Zang Q., Sadka A, Li L., & Zhou H. (2019). Inverse boosting pruning trees for depression detection on Twitter. arXiv preprint. https://doi.org/10.48550/arXiv.1906.00398.
Andrade L., Caraveo‐Anduaga J. J., Berglund P., Bijl R. V., Graaf R. D., Vollebergh W. & Wittchen H. U. (2003). The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. International journal of methods in psychiatric research. 12(1);3-21. https://doi.org/10.1002/mpr.138. DOI: https://doi.org/10.1002/mpr.138
Peng Z., Hu Q. & Dang J. (2019). Multi-kernel SVM based depression recognition using social media data. International Journal of Machine Learning and Cybernetics. 10(1);43-57. https://doi.org/10.1007/s13042-017-0697-1. DOI: https://doi.org/10.1007/s13042-017-0697-1
Ledford H. (2014). Medical research: if depression were cancer. Nature News. 515(7526), 182. https://doi.org/10.1038/515182a. DOI: https://doi.org/10.1038/515182a
Smith K. (2014). Mental health: a world of depression. Nature News. 515(7526);180-181. https://doi.org/10.1038/515180a. DOI: https://doi.org/10.1038/515180a
Hassan, A. U., Hussain, J., Hussain, M., Sadiq, M., & Lee, S. (2017). Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. 2017 International Conference on Information and Communication Technology Convergence (ICTC). 138–140. https://doi.org/10.1109/ictc.2017.8190959. DOI: https://doi.org/10.1109/ICTC.2017.8190959
Trotzek, M., Koitka, S., & Friedrich, C. M. (2020). Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences. IEEE Transactions on Knowledge and Data Engineering. 32(3);588–601. https://doi.org/10.1109/tkde.2018.2885515.
[8] Gowen K., Deschaine M., Gruttadara D. & Markey D. (2012). Young adults with mental health conditions and social networking websites: Seeking tools to build community. Psychiatric Rehabilitation Journal. 35(3);245. https://doi.org/10.2975/35.3.2012.245.250. DOI: https://doi.org/10.2975/35.3.2012.245.250
Naslund J. A., Grande S. W., Aschbrenner K. A. & Elwyn G. (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PLOS one. 9(10). https://doi.org/10.1371/journal.pone.0110171. DOI: https://doi.org/10.1371/journal.pone.0110171
Abhyankar A. (2011). Social networking sites. SAMVAD. 2,;18-21. DOI: https://doi.org/10.4018/jvple.2011070102
Braithwaite S.R., Giraud-Carrier C., West J., Barnes M. D. & Hanson C.L. (2016). Validating machine learning algorithms for Twitter data against established measures of suicidality. JMIR mental health. 3(2) , e21. https://doi.org/10.2196/mental.4822. DOI: https://doi.org/10.2196/mental.4822
Hussain J., Ali M., Bilal H.S.M., Afzal M., Ahmad H.F., Banos O., & Lee S. (2015). SNS based predictive model for depression. In International Conference on Smart Homes and Health Telematics. 349-354. https://doi.org/10.1007/978-3-319-19312-0_34. DOI: https://doi.org/10.1007/978-3-319-19312-0_34
Naslund J.A., Bondre A., Torous J., & Aschbrenner K.A. (2020). Social media and mental health: Benefits, risks, and opportunities for research and practice. Journal of technology in behavioral science. 5(3);245-257. https://doi.org/10.1007/s41347-020-00134-x DOI: https://doi.org/10.1007/s41347-020-00134-x
Ahmed Y.A., Ahmad M.N., Ahmad N., & Zakaria N. H. (2019). Social media for knowledge-sharing: A systematic literature review. Telematics and informatics. 37;72-112. https://doi.org/10.1016/j.tele.2018.01.015. DOI: https://doi.org/10.1016/j.tele.2018.01.015
Chen, Y., Zhou, B., Zhang, W., Gong, W., & Sun, G. (2018). Sentiment Analysis Based on Deep Learning and Its Application in Screening for Perinatal Depression. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). 451–456. https://doi.org/10.1109/dsc.2018.00073.
Edition F. (2013). Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc, 21.
Yao H., Ye R., Jing W. & Ting W. (2016). Clinical analyses of risk factors in perinatal pregnant women with depression, JClinPsychosom, 28-31.
Ruess J., Parise F., Milias-Argeitis A., Khammash M. & Lygeros J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. Proceedings of the National Academy of Sciences. 112(26);8148-8153. https://doi.org/10.1073/pnas.1423947112. DOI: https://doi.org/10.1073/pnas.1423947112
Duggan M. & Smith A. (2013). Social media update. Pew Internet and American Life Project. http://pewinternet.org/Reports/2013/Social-MediaUpdate.aspx, 2013.
Brenner J. (2013). Pew internet: social networking. Pew Internet and American Life Project. http://www.pewinternet.org/factsheets/socialnetworking-fact-sheet/, 2013.
Edwards J.(2013). Facebook is no longer the most popular social network for teens. Business Insider. http://www.businessinsider.com/ facebook-and-teen-user-trends-2013.
Duggan M. (2015). Social media update 2014. Pew Research Centre. http://www.pewinternet.org/2015/01/09/social-media-update-2014.
Marwick, A. E., & boyd, danah. (2010). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society. 13(1);114–133. https://doi.org/10.1177/1461444810365313. DOI: https://doi.org/10.1177/1461444810365313
Shapero B.G., Black S.K., Liu R.T., Klugman J., Bender R.E., Abramson L.Y. & Alloy L.B. (2014). Stressful life events and depression symptoms: The effect of childhood emotional abuse on stress reactivity. Journal of Clinical Psychology. 70;209-223. https://doi.org/10.1002/jclp.22011. DOI: https://doi.org/10.1002/jclp.22011
Zlotnick C., Kohn R., Keitner G. & Della Grotta S.A. (2000). The relationship between quality of interpersonal relationships and major depressive disorder: Findings from the national comorbidity survey. Journal of Affective Disorders. 59(3);205–215. https://doi.org/10.1016/s0165-0327(99)00153-6. DOI: https://doi.org/10.1016/S0165-0327(99)00153-6
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5) American psychiatric pub. DOI: https://doi.org/10.1176/appi.books.9780890425596
Tekell J.L., Hoffmann R., Hendrickse W., Greene R.W., Rush A.J. & Armitage R. (2005). High frequency EEG activity during sleep: Characteristics in schizophrenia and depression. Clinical EEG and Neuroscience. 36(1);25–35. https://doi.org/10.1177/155005940503600107. DOI: https://doi.org/10.1177/155005940503600107
Low L.S.A., Maddage N.C., Lech M., Sheeber L. & Allen N. (2010). Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents. In: Proceedings of IEEE international conference on acoustics, speech and signal processing. 5154–5157. https://doi.org/10.1109/icassp.2010.5495018. DOI: https://doi.org/10.1109/ICASSP.2010.5495018
Cohn J.F., Kruez T.S., Matthews I., Yang Y., Nguyen M.H., Padilla M.T., Zhou F. & De la Torre F. (2009). Detecting depression from facial actions and vocal prosody. In: Proceedings of IEEE international conference on affective computing and intelligent interaction and workshops. 1–7. https://doi.org/10.1109/acii.2009.5349358. DOI: https://doi.org/10.1109/ACII.2009.5349358
Tong L., Zhang Q., Sadka A., Li L. & Zhou H. (2019). Inverse boosting pruning trees for depression detection on Twitter. arXiv preprint. https://doi.org/10.48550/arXiv.1906.00398.
Bilal, U., & Khan, F. H. (2020). An Analysis of Depression Detection Techniques from Online Social Networks. Intelligent Technologies and Applications. 296–308. https://doi.org/10.1007/978-981-15-5232-8_26. DOI: https://doi.org/10.1007/978-981-15-5232-8_26
Mustafa, R. U., Ashraf, N., Ahmed, F. S., Ferzund, J., Shahzad, B., & Gelbukh, A. (2020). A Multiclass Depression Detection in Social Media Based on Sentiment Analysis. 17th International Conference on Information Technology–New Generations (ITNG 2020). 659–662. https://doi.org/10.1007/978-3-030-43020-7_89. DOI: https://doi.org/10.1007/978-3-030-43020-7_89
Verma, B., Gupta, S., & Goel, L. (2020). A Neural Network Based Hybrid Model for Depression Detection in Twitter. Advances in Computing and Data Sciences. 164–175. https://doi.org/10.1007/978-981-15-6634-9_16 . DOI: https://doi.org/10.1007/978-981-15-6634-9_16
Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of Depression-Related Posts in Reddit Social Media Forum. IEEE Access. 7;44883–44893. https://doi.org/10.1109/access.2019.2909180. DOI: https://doi.org/10.1109/ACCESS.2019.2909180
Pampouchidou, A., Simos, P. G., Marias, K., Meriaudeau, F., Yang, F., Pediaditis, M., & Tsiknakis, M. (2019). Automatic Assessment of Depression Based on Visual Cues: A Systematic Review. IEEE Transactions on Affective Computing. 10(4);445–470. https://doi.org/10.1109/taffc.2017.2724035. DOI: https://doi.org/10.1109/TAFFC.2017.2724035
Rao, G., Zhang, Y., Zhang, L., Cong, Q., & Feng, Z. (2020). MGL-CNN: A Hierarchical Posts Representations Model for Identifying Depressed Individuals in Online Forums. IEEE Access. 8; 32395–32403. https://doi.org/10.1109/access.2020.2973737. DOI: https://doi.org/10.1109/ACCESS.2020.2973737
Islam M.R., Kabir M.A., Ahmed A., Kamal A.R.M., Wang H. & Ulhaq A. (2018). Depression detection from social network data using machine learning techniques. Health Information Science Systems. 6(1);1-12. https://doi.org/10.1007/s13755-018-0046-0. DOI: https://doi.org/10.1007/s13755-018-0046-0
Deshpande, M., & Rao, V. (2017). Depression detection using emotion artificial intelligence. 2017 International Conference on Intelligent Sustainable Systems (ICISS). 858-862. https://doi.org/10.1109/iss1.2017.8389299. DOI: https://doi.org/10.1109/ISS1.2017.8389299
Trotzek M., Koitka S. & Friedrich C.M. (2018). Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans. on Knowledge and Data Engineering. 32(3);588-601. https://doi.org/10.1109/tkde.2018.2885515. DOI: https://doi.org/10.1109/TKDE.2018.2885515
Chen Y., Zhou B., Zhang W., Gong W. & Sun G. (2018). Sentiment analysis based on deep learning and its application in screening for perinatal depression. In: Proceedings IEEE 3rd International Conference on Data Science in Cyberspace. 451-456. https://doi.org/10.1109/dsc.2018.00073. DOI: https://doi.org/10.1109/DSC.2018.00073
Stephen J.J. & Prabu P. (2019). Detecting the magnitude of depression in Twitter users using sentiment analysis. In: Proceedings IEEE international journal of electrical and computer engineering. 9(4);3247-3255. https://doi.org/10.11591/ijece.v9i4.pp3247-3255. DOI: https://doi.org/10.11591/ijece.v9i4.pp3247-3255
Almouzini S. & Alageel A. (2019). Detecting Arabic depressed users from Twitter data. Procedia Computer Science. 163;257–265. https://doi.org/10.1016/j.procs.2019.12.107. DOI: https://doi.org/10.1016/j.procs.2019.12.107
Kumar A., Sharma A. & Arora A. (2019). Anxious depression prediction in real-time social data. In International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019. https://doi.org/10.2139/ssrn.3383359. DOI: https://doi.org/10.2139/ssrn.3383359
Shrestha K. (2018). Machine Learning for Depression Diagnosis using Twitter data. International Journal of Computer Engineering in Research Trends. 2(5), 56-61.
Bhargava S. & Choudhary S. (2018). Behavioral Analysis of Depressed Sentimental Over Twitter: Based on Supervised Machine Learning Approach. In: Proceedings of 3rd International Conference on Internet of Things and Connected Technologies. 26-27. https://doi.org/10.2139/ssrn.3175816. DOI: https://doi.org/10.2139/ssrn.3175816
Orabi A.H., Buddhitha P., Orabi M.H. & Inkpen D. (2018). Deep learning for depression detection of twitter users. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 88-97. https://doi.org/10.18653/v1/w18-0609. DOI: https://doi.org/10.18653/v1/W18-0609
Zogan H., Razzak I., Wang X., Jameel S. & Xu G. (2022). Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web. 25(1);281-304. https://doi.org/10.1007/s11280-021-00992-2. DOI: https://doi.org/10.1007/s11280-021-00992-2
Tirtopangarsa A.P. & Maharani W. (2021). Sentiment Analysis of Depression Detection on Twitter Social Media Users Using the K-Nearest Neighbor Method. In Seminar Nasional Informatika (SEMNASIF). 1(1);247-258.
Loh, H.W., Ooi C.P., Aydemir E., Tuncer T., Dogan S. & Acharya U.R. (2021). Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals. Expert Systems. 39(3). https://doi.org/10.1111/exsy.12773. DOI: https://doi.org/10.1111/exsy.12773
Akbari H., Sadiq M.T. & Rehman A.U. (2021). Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Information Science and Systems. 9(1);1-15. https://doi.org/10.1007/s13755-021-00139-7. DOI: https://doi.org/10.1007/s13755-021-00139-7
Ferraro G., Loo Gee B., Ji S. & Salvador-Carulla L. (2020). Lightme: analysing language in internet support groups for mental health. Health Information Science and Systems. 8(1);1-10. https://doi.org/10.1007/s13755-020-00115-7. DOI: https://doi.org/10.1007/s13755-020-00115-7
Amanat A., Rizwan M., Javed A.R., Abdelhaq M., Alsaqour R., Pandya S. & Uddin M. (2022). Deep learning for depression detection from textual data. Electronics. 11(5);676. https://doi.org/10.3390/electronics11050676. DOI: https://doi.org/10.3390/electronics11050676
Mostafa A.M., Aljasir M., Alruily M., Alsayat A. & Ezz M. (2023). Innovative Forward fusion feature selection algorithm for sentiment analysis using supervised classification. Applied Sciences. 13(4);2074. https://doi.org/10.3390/app13042074. DOI: https://doi.org/10.3390/app13042074
Obagbuwa I.C., Danster S. & Chibaya O.C. (2023). Supervised machine learning models for depression sentiment analysis. Frontiers in Artificial Intelligence. 6;1230649. https://doi.org/10.3389/frai.2023.1230649. DOI: https://doi.org/10.3389/frai.2023.1230649
K. Tamilselvan, , M. N. S., A. Saranya, D. Abdul Jaleel, Er. Tatiraju V. Rajani Kanth, & S.D. Govardhan. (2025). Optimizing data processing in big data systems using hybrid machine learning techniques. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.936
Anakal, S., K. Krishna Prasad, Chandrashekhar Uppin, & M. Dileep Kumar. (2025). Diagnosis, visualisation and analysis of COVID-19 using Machine learning. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.826 DOI: https://doi.org/10.22399/ijcesen.826
Wang, S., & Koning, S. bin I. (2025). Social and Cognitive Predictors of Collaborative Learning in Music Ensembles. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.806 DOI: https://doi.org/10.22399/ijcesen.806
S. Leelavathy, S. Balakrishnan, M. Manikandan, J. Palanimeera, K. Mohana Prabha, & R. Vidhya. (2024). Deep Learning Algorithm Design for Discovery and Dysfunction of Landmines. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.686 DOI: https://doi.org/10.22399/ijcesen.686
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
