Abstract
Depression and other forms of mental illness are relatively common, and it has been shown that these conditions have an effect on an entity's physical health. Newly, artificial intelligence (AI) technologies have been created to aid mental health practitioners, such as psychiatrists and psychologists, in decision-making based on the historic data of patients (for example, medical records, behavioral data, social media use, etc.). These AI methods are intended to help mental health clinicians treat patients more effectively. One of the most recent generations of AI technologies, deep learning (DL), has exhibited greater performance in a wide variety of real-world applications spanning from computer vision to health care. When adopting bidirectional encoder representations from transformers (Enhanced BERT), the authors of the current research offer a new framework that may quickly and accurately identify postings that are connected to anxiety and depression. In addition, an intelligence distillation approach is a present method for transferring information from a large pretrained model BERT to a reduced model in instruction to expand the performance and accuracy of the smaller model. Researchers made use of word2vec and BERT in order to effectively analyze and identify symptoms of melancholy and anxiety based on our very own 40,000 data collecting infrastructure based on Twitter, the most widely used of the social media platforms. Using the Enhanced BERT methodology, our system achieves an accuracy of 92%, which is difficult than any other state-of-the-art technology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shatte ABR, Hutchinson DM, Teague SJ (2019) Machine learning in mental health: a sco** review of methods and applications. Psychol Med 49(09):1426–1448. https://doi.org/10.1017/s0033291719000151
Durstewitz D, Koppe G, Meyer-Lindenberg A (2019) Deep neural networks in psychiatry. Mol Psychiatry 24(11):1583–1598. https://doi.org/10.1038/s41380-019-0365-9
Kroenke K, Spitzer RL, Williams JBW (2003) The patient health questionnaire-2. Med Care 41(11):1284–1292. https://doi.org/10.1097/01.mlr.0000093487.78664.3c
von Glischinski M, Teismann T, Prinz S, Gebauer JE, Hirschfeld G (2016) Depressive symptom inventory suicidality subscale: optimal cut points for clinical and non-clinical samples. Clin Psychol Psychother 23(6):543–549. https://doi.org/10.1002/cpp.2007
Marcus M, Yasamy MT, van van Ommeren M, Chisholm D, Saxena S (2012) Depression: a global public health concern. PsycEXTRA dataset. https://doi.org/10.1037/e517532013-004
Meier T et al (2019) ‘LIWC auf Deutsch’: the development, psychometrics, and introduction of DE-LIWC2015. https://doi.org/10.31234/osf.io/uq8zt
Trotzek M, Koitka S, Friedrich CM (2020) Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans Knowl Data Eng 32(3):588–601. https://doi.org/10.1109/tkde.2018.2885515
Matero M et al (2019) Suicide risk assessment with multi-level dual-context language and BERT. In: Proceedings of the sixth workshop on computational linguistics and clinical psychology. https://doi.org/10.18653/v1/w19-3005
William D, Suhartono D (2021) Text-based depression detection on social media posts: a systematic literature review. Procedia Comput Sci 179:582–589. https://doi.org/10.1016/j.procs.2021.01.043
Tadesse MM, 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
Dalal S, Jain S, Dave M (2023) An Investigation of data requirements for the detection of depression from social media posts. Recent Patents Eng 17(3). https://doi.org/10.2174/1872212117666220812110956
Li M, Lim KH (2022) Geotagging social media posts to landmarks using hierarchical BERT (Student Abstract). Proc AAAI Conf Artif Intell 36(11):12999–13000. https://doi.org/10.1609/aaai.v36i11.21636
Kumar Singh K (2023) Study of early risks of depression by analysing social media posts. IIMS J Manag Sci 14(1):9–25. https://doi.org/10.1177/0976030x221112529
Patidar H, Umre J (2021) Predicting depression level using social media posts. Int J Res Granthaalayah 8(12):234–237. https://doi.org/10.29121/granthaalayah.v8.i12.2020.1972
Gupta S, Goel L, Singh A, Prasad A, Ullah MA (2022) Psychological analysis for depression detection from social networking sites. Comput Intell Neurosci 2022:1–14. https://doi.org/10.1155/2022/4395358
Raja MS, Raj LA, Arun A (2022) Detection of depression among social media users with machine learning. Webology 19(1):250–257. https://doi.org/10.14704/web/v19i1/web19019
Nareshkumar R, Nimala K (2023) Interactive deep neural network for aspect-level sentiment analysis. In: 2023 International conference on artificial intelligence and knowledge discovery in concurrent engineering (ICECONF). https://doi.org/10.1109/iceconf57129.2023.10083812
Nareshkumar R, Agalya K, Arunpandiyan A, Vijayalakshmi M, Ranjani V, Ramya A (2023) An effective deep learning based recommender system with user and item embedding. In: 2023 International conference on artificial intelligence and knowledge discovery in concurrent engineering (ICECONF). https://doi.org/10.1109/iceconf57129.2023.10083578
Sailesh LJ, Kumar VK, Nimala K, Nareshkumar R (2023) Emotion detection in instagram social media platform. In: 2023 international conference on artificial intelligence and knowledge discovery in concurrent engineering (ICECONF). https://doi.org/10.1109/iceconf57129.2023.10083724
Nareshkumar R, Nimala K (2022) An exploration of intelligent deep learning models for fine grained aspect-based opinion mining. In: 2022 international conference on innovative computing, intelligent communication and smart electrical systems (ICSES). https://doi.org/10.1109/icses55317.2022.9914094
Sirenjeevi P, Karthick JM, Agalya K, Srikanth R, Elangovan T, Nareshkumar R (2023) Leaf disease identification using ResNet. In: 2023 international conference on artificial intelligence and knowledge discovery in concurrent engineering (ICECONF). https://doi.org/10.1109/iceconf57129.2023.10083963
Nareshkumar R, Suseela G, Nimala K, Niranjana G (2022) Feasibility and necessity of affective computing in emotion sensing of drivers for improved road safety. In: Advances in computational intelligence and robotics, pp 94–115. https://doi.org/10.4018/978-1-6684-3843-5.ch007
Nareshkumar R, Nimala K (2023) Interactive deep neural network for aspect-level sentiment analysis. In: 2023 international conference on artificial intelligence and knowledge discovery in concurrent engineering (ICECONF). Chennai, India, 2023, pp 1–8. https://doi.org/10.1109/ICECONF57129.2023.10083812
De Choudhury M, Gamon M, Counts S, Horvitz E (2021) Predicting depression via social media. Proc Int AAAI Conf Web Soc Media 7(1):128–137. https://doi.org/10.1609/icwsm.v7i1.14432
Reece AG, Reagan AJ, Lix KLM, Dodds PS, Danforth CM, Langer EJ (2017) Forecasting the onset and course of mental illness with Twitter data. Sci Rep 7(1). https://doi.org/10.1038/s41598-017-12961-9
Naseem U, Dunn AG, Kim J, Khushi M (2022) Early identification of depression severity levels on reddit using ordinal classification. In: Proceedings of the ACM web conference 2022. https://doi.org/10.1145/3485447.3512128
Ren F, Kang X, Quan C (2016) Examining accumulated emotional traits in suicide blogs with an emotion topic model. IEEE J Biomed Health Inform 20(5):1384–1396. https://doi.org/10.1109/jbhi.2015.2459683
Zhou T, Hu G, Wang L (2019) Psychological disorder identifying method based on emotion perception over social networks. Int J Environ Res Public Health 16(6):953. https://doi.org/10.3390/ijerph16060953
Razak CSA, Zulkarnain MA, Hamid SHA, Anuar NB, Jali MZ, Meon H (2020) Tweep: a system development to detect depression in Twitter posts. Comput Sci Technol 543–552. https://doi.org/10.1007/978-981-15-0058-9_52
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nareshkumar, R., Nimala, K. (2024). An Enhanced BERT Model for Depression Detection on Social Media Posts. In: Sharma, H., Chakravorty, A., Hussain, S., Kumari, R. (eds) Artificial Intelligence: Theory and Applications. AITA 2023. Lecture Notes in Networks and Systems, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-99-8479-4_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-8479-4_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8478-7
Online ISBN: 978-981-99-8479-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)