Stress Detection Using NLP and DL Models

DOI

10.2991/978-94-6463-718-2_147How to use a DOI?

Keywords

BERT Model; SVM; XG-Boost; deep learning; machine learning; and natural language processing

Abstract

Many people use social media platforms these days to post tweets about their everyday lives that reveal their mental health. Stress must be identified and dealt with before it becomes a serious issue. a considerable of casual communications shared every day on blogs, chat rooms, and social networking sites. This study suggests a method for calculating stress levels through data which were from the social media like Twitter. This project handled data collection, cleaning, system training, and determining people’s stress levels. Accomplished by utilizing the algorithms or approaches and several other models like natural language processing (NLP) and ML models, XG-Boost, Random Forest, Decision Tree, SVM, Bernoulli-NB. People’s health is at risk due to psychological stress. Proactive care necessitates quick stress assessment. Online social network data can be utilized to detect stress because users are accustomed to interacting with friends and sharing details of their everyday lives on these sites. Heavy, we find a considerable relationship between the stress level of the person and their friends on social media. Our systematic methodology makes use of a sizable dataset from actual social networks. In order to improve outcomes, we first develop a series of stress-related tests and then use machine learning techniques to train the system. After that, the suggested system determines whether or not to highlight a tweet depending on the input tweets.

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.

Download article (PDF)

TY  - CONF
AU  - Anil Vithalrao Turukmane
AU  - Lakshmi Narayana Avula
AU  - Chaitanya Gunji
AU  - Reddy Nithish Kumar Devapatla
AU  - Somnath Ambati
PY  - 2025
DA  - 2025/05/23
TI  - Stress Detection Using NLP and DL Models
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1776
EP  - 1788
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_147
DO  - 10.2991/978-94-6463-718-2_147
ID  - Turukmane2025
ER  -