Decoding Sarcasm: Machine Learning and Its Effect on Mental Health

DOI

10.2991/978-94-6463-738-0_91How to use a DOI?

Keywords

Artificial Intelligence (AI); Machine Learning (ML); depression; suicide; anxiety; PTSD (Post-Traumatic Stress Disorder); bipolar disorder; Neural Network (NN) and Deep Neural Network (DNN)

Abstract

A binary classification model was analyzed for its performance measurements of depression, suicide, anxiety, PTSD and bipolar disorder. Each mental health condition was evaluated through precision and F1-score indicators to measure model performance with accuracy as an additional evaluation factor. Performance levels differ among the various categories according to the results. The F1-score reached 0.80 because anxiety achieved a precision rate of 0.82 along with a balanced recall of 0.85. Suicide and bipolar disorder displayed the least successful identification through F1-scores (0.74 and 0.73) because precision measurements were unsatisfactory which leads to difficulties in accurate diagnosis. The scores obtained for PTSD and depression manifested moderate levels as their F1-scores fell between 0.77–0.79. A prognosis accuracy resulting from the model reached 0.79. The evaluation results demonstrate that the model shows strong potential for mental health condition detection but present difficulties in precise diagnosis of suicide cases and bipolar disorders. Future research will handle these restrictions to establish a stronger and more dependable classification model.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Swati Sharma
AU  - Priya Batta
PY  - 2025
DA  - 2025/06/22
TI  - Decoding Sarcasm: Machine Learning and Its Effect on Mental Health
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 1189
EP  - 1207
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_91
DO  - 10.2991/978-94-6463-738-0_91
ID  - Sharma2025
ER  -