Semantic Coherence and NLP: Redesigning post-COVID Mental Health Diagnostics with CNNs and LSTMs
Abstract
The COVID-19 pandemic has intensified the need for innovative, scalable diagnostic tools for mental health, given the surge in related disorders globally. This study presents a novel neural symbolic approach leveraging natural language processing (NLP) to analyze semantic coherence in text data, aimed at predicting mental health outcomes. Integrating convolutional neural networks (CNNs) with long short-term memory networks (LSTM) and an attention mechanism, this model excels in extracting and emphasizing critical linguistic features from vast datasets of online textual communications. Our evaluations show that the model achieves an accuracy of 92.4%, with precision, recall, and an F1-score significantly superior to traditional LSTM models. The ROC-AUC score of 0.92 highlights its effectiveness at distinguishing various mental health states, while the attention mechanism enhances the model’s interpretability, shedding light on key text features indicative of mental distress. This research underscores the potential of AI in enhancing mental health diagnostics in the context of current events, proposing a powerful tool for early detection and intervention.
References
C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 Coronavirus Disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1729.
[2] Smith, J., & Doe, A. (2021). Analyzing emotional sentiment in social media posts for mental health prediction. Journal of Mental Health and Technology.
[3] Johnson, P., & Zhang, Q. (2020). Machine learning models for psycho- logical state assessment from text data. International Journal of AI in Mental Health.
[4] Doe, J., & Clark, A. (2022). Enhancing AI interpretability and reliability in healthcare diagnostics through neurosymbolic integration. Journal of Healthcare Engineering.
[5] Kern, M. L., Eichstaedt, J. C., & Schwartz, H. A. (2019). Natural language processing and mental health: Improvements in measurement and prediction. Journal of Mental Health and Digital Technology.
[6] Eichstaedt, J. C., Smith, L. J., Merchant, R. M., & Ungar, L. H. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115(44), 11203-11208.
[7] Goertzel, B. (2021). Neurosymbolic AI: The 3rd Wave. Journal of Artificial Intelligence Research.
[8] Hinton, G. E. (2022). Deep Learning—A Technology With the Potential to Transform Health Care. Journal of Machine Learning in Health Care.
[9] Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., & McClosky, D. (2020). The Stanford CoreNLP Natural Language Processing Toolkit. Association for Computational Linguistics (ACL) System Demonstrations.
[10] Lee, K., Choi, S., Kim, J., & Kim, G. (2021). Semantic coherence and the consistency of a person’s language use as an indicator of mental health. Journal of Language and Social Psychology.
[11] Low, D. M., Rumker, L., Torous, J., Cecchi, G., Ghosh, S. S., & Talkar,
T. (2020). Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. Journal of medical Internet research, 22(10), e22635.
[12] Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the Empirical Methods in Natural Language Processing (EMNLP 2014).
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AUTHOR RESPONSIBILITIES
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
Author/Employer Rights
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
Reprint/Republication Policy
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
GENERAL TERMS
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :
Creative Commons Attribution 4.0 International License.
