Psychological Stress Detection Using Transformer-Based Models
DOI:
https://doi.org/10.21512/comtech.v15i1.11105Keywords:
stress detection, transformer-based model, Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT (RoBERTa)Abstract
Stress is a significant mental health problem that results in a lack of concentration. It has been more widely identified through social media since people who are under stress usually post about their physical pain and tiredness. However, stress assessment through social media by professionals can be expensive and time-consuming. The research aimed to produce a stress detection system trained using a Twitter dataset to predict stress using the user’s input sentence. The experiments that were done in the research used transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT (RoBERTa). The research involved data pre-processing, model training, and model evaluation to ensure high-quality train data. Since the data were imbalanced, data trimming was performed in pre-processing to select data randomly until the balance matched. This process ensured the model’s effectiveness in the training and evaluation stages. The features used in these experiments were features from each pre-trained model. In evaluating the model, accuracy, loss, and F1 score were used as metrics. In the result, for BERT, accuracy reaches 0.848 with an F1 score of 0.847. Meanwhile, RoBERTa has an accuracy of 0.837 and 0.834. The results prove that BERT and RoBERTa can be used to classify stress with accuracy and an F1 score above 0.8. The experiment result shows that the BERT deep learning model can detect stress using the Twitter datasets.
Plum Analytics
References
Aalbers, G., McNally, R. J., Heeren, A., De Wit, S., & Fried, E. I. (2019). Social media and depression symptoms: A network perspective. Journal of Experimental Psychology: General, 148(8), 1454–1462. https://doi.org/10.1037/xge0000528
Areshey, A., & Mathkour, H. (2023). Transfer learning for sentiment classification using Bidirectional Encoder Representations from Transformers (BERT) model. Sensors, 23(11), 1–18. https://doi.org/10.3390/s23115232
American Psychological Association. (2020, October). Stress in America 2020: A national mental health crisis. https://www.apa.org/news/press/releases/stress/2020/report-october
Bhimani, H., Mention, A. L., & Barlatier, P. J. (2019). Social media and innovation: A systematic literature review and future research directions. Technological Forecasting and Social Change, 144, 251–269. https://doi.org/10.1016/j.techfore.2018.10.007
Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226. https://doi.org/10.1016/j.knosys.2021.107134
García, V., Sánchez, J. S., Marqués, A. I., Florencia, R., & Rivera, G. (2020). Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data. Expert Systems with Applications, 158. https://doi.org/10.1016/j.eswa.2019.113026
Gedam, S., & Paul, S. (2021). A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045–84066. https://doi.org/10.1109/ACCESS.2021.3085502
Guntuku, S. C., Buffone, A., Jaidka, K., Eichstaedt, J. C., & Ungar, L. H. (2019). Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, pp. 214–225). https://doi.org/10.1609/icwsm.v13i01.3223
Hampton, K. N., Rainie, L., Lu, W., Dwyer, M., Shin, I., & Purcell, K. (2014, August 26). Social media and the spiral of silence. PewResearchCenter. https://www.pewresearch.org/internet/2014/08/26/social-media-and-the-spiral-of-silence/
Hasan, M. R., Maliha, M., & Arifuzzaman, M. (2019). Sentiment analysis with NLP on Twitter data. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (pp. 1–4). IEEE. https://doi.org/https://doi.org/10.1109/IC4ME247184.2019.9036670
Hawk, S. T., Van Den Eijnden, R. J., Van Lissa, C. J., & Ter Bogt, T. F. (2019). Narcissistic adolescents' attention-seeking following social rejection: Links with social media disclosure, problematic social media use, and smartphone stress. Computers in Human Behavior, 92, 65–75. https://doi.org/10.1016/j.chb.2018.10.032
Inamdar, S., Chapekar, R., Gite, S., & Pradhan, B. (2023). Machine learning driven mental stress detection on Reddit posts using natural language processing. Human-Centric Intelligent Systems, 3(2), 80–91. https://doi.org/10.1007/s44230-023-00020-8
Joshy, A., & Sundar, S. (2022). Analyzing the performance of sentiment analysis using BERT, DistilBERT, and RoBERTa. In 2022 IEEE International Power and Renewable Energy Conference (IPRECON) (pp. 1–6). IEEE. https://doi.org/10.1109/IPRECON55716.2022.10059542
Maslej-Krešňáková, V., Sarnovský, M., Butka, P., & Machová, K. (2020). Comparison of deep learning models and various text pre-processing techniques for the toxic comments classification. Applied Sciences, 10(23), 1–26. https://doi.org/10.3390/app10238631
Oryngozha, N., Shamoi, P., & Igali, A. (2024). Detection and analysis of stress-related posts in Reddit’s acamedic communities. IEEE Access, 12, 14932–14948. https://doi.org/10.1109/ACCESS.2024.3357662
Rastogi, A., Liu, Q., & Cambria, E. (2022). Stress detection from social media articles: New dataset benchmark and analytical study. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892889
Ríssola, E. A., Losada, D. E., & Crestani, F. (2021). A survey of computational methods for online mental state assessment on social media. ACM Transactions on Computing for Healthcare, 2(2), 1–31. https://doi.org/10.1145/3437259
Samele, C., Lees-Manning, H., Zamperoni, V., Goldie, I., Thorpe, L., Wooster, E., ... & Rowland, M. (2018). Stress: Are we coping. Mental Health Foundation.
Wang, W., Chen, L., Thirunarayan, K., & Sheth, A. P. (2012). Harnessing Twitter “big data” for automatic emotion identification. In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing (pp. 587–592). IEEE. https://doi.org/10.1109/SocialCom-PASSAT.2012.119
Winata, G. I., Kampman, O. P., & Fung, P. (2018). Attention-based LSTM for psychological stress detection from spoken language using distant supervision. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6204–6208). IEEE. https://doi.org/10.1109/ICASSP.2018.8461990
Yogish, D., Manjunath, T. N., & Hegadi, R. S. (2019). Review on natural language processing trends and techniques using NLTK. In Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018 (pp. 589-606). Springer Singapore. https://doi.org/10.1007/978-981-13-9187-3_53
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Derwin Suhartono, Irfan Fahmi Saputra, Andhika Rizki Pratama, Gabriel Nathaniel
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: