Summary
The project focuses on using AI and text analysis to monitor mental health through social media. It involves creating a sentiment analysis tool to detect emotional patterns and deploying it on the cloud for wide-scale use.
Project overview
Overview:
This project explores how technology can support mental health by analyzing sentiment in social media text. By leveraging machine learning and natural language processing (NLP), it demonstrates how online behavior can provide insights into mental well-being.
Key Highlights:
- Tools Used: Python, Machine Learning, Natural Language Processing, and Streamlit.
- Objective: To create a scalable solution that monitors mental health through sentiment analysis of social media data.
- Process: Built a data pipeline for preprocessing, feature extraction, and sentiment detection using supervised machine learning models.
- Impact: Achieved a 75% accuracy rate on a dataset of 100,000 entries, showcasing the system’s ability to detect early signs of mental health challenges.
- Scalability: Deployed the solution on the cloud to ensure it can handle large volumes of data efficiently.
This project highlights the potential of AI-powered tools in driving meaningful interventions through online behavioral insights.
