Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{153330,
        author = {VASAM DIVYA MOUNIKA and M. CHIRANJEEVI},
        title = {STRESS ANALYSIS ON SOCIAL INTERACTIONS ON SOCIAL NETWORKING SITES USING MACHINE AND DEEP LEARNING HYBRID CLASSIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {6},
        pages = {452-459},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153330},
        abstract = {Social media currently plays an important role in current social structure of humanity. People are used to sharing their daily activities and interacting with friends on social media platforms which provides a unique opportunity with mining, measuring, modeling different user behavioural patterns. The project is based on the idea of detecting the users stress in a proactive manner. The proposed system uses ensemble learning model which gives better accuracy compared to other standard machine learning models with the help of meta classifier and the applied algorithms are KNN, Random Forest, Convolution Neural Network, Support Vector Machine.},
        keywords = {Stress detection, factor graph model, micro-blog, social media, healthcare, social interaction},
        month = {},
        }