Copyright © 2026 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{176798,
        author = {Himanshi Paisal and Siddharth Verma},
        title = {AI-Based Depression Detection Using NLP and Social Media Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7589-7592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176798},
        abstract = {Globally, depression is becoming a more serious mental health issue, yet early detection is still difficult because of stigma and ignorance. This study investigates the use of natural language processing (NLP) and artificial intelligence (AI) for social media content analysis in order to identify signs of depression. We create and evaluate different machine learning (ML) and deep learning models, such as Support Vector Machines (SVM), Random Forest, LSTM, and BERT, using sentiment analysis, emotion identification, and language pattern recognition. The findings demonstrate that when it comes to correctly detecting depressive tendencies, deep learning models—BERT in particular—perform better than conventional machine learning models. This study emphasises the ethical ramifications of AI-driven mental health monitoring, including privacy issues and bias in AI models, as well as the potential of this technology.},
        keywords = {},
        month = {April},
        }