Mental Health Sentiment Analysis Using Social Media Texts

This project aims to analyze public sentiment related to mental health and suicide by processing text data from social media and other open sources. It uses natural language processing (NLP) and machine learning techniques to classify user-generated content into positive, negative, and neutral sentiments. The goal is to identify emotional patterns that could help in early detection of mental distress and promote timely support.

The repository contains:

Cleaned and preprocessed dataset (.csv)

Python code for sentiment analysis

Model training scripts using scikit-learn and/or deep learning

Visualizations for data exploration and sentiment trends

This resource can be valuable for researchers, mental health professionals, and developers building mental health-aware AI systems.

Disclaimer: This dataset is for academic and research purposes only. It does not replace clinical advice or diagnosis.