Inspiration
Many people experience stress, anxiety, or burnout but don’t realize it until it starts affecting their daily life. I wanted to build something simple that could help people understand their emotional state by analyzing the text they already write, like journal entries or chat messages.
What It Does
Emotion & Wellness Analyzer is a web app that analyzes text to identify emotions, sentiment, and stress-related patterns. Based on this analysis, it provides a wellness score and suggests simple actions to improve mental well-being.
How I Built It
I built the backend using FastAPI and used NLP libraries such as NLTK, TextBlob, and VADER Sentiment Analysis to process and analyze text.
The frontend is built with React and Vite, and I used Recharts to visualize emotional trends and Lucide React for a clean and modern interface.
The app communicates through API endpoints that handle emotion analysis, pattern tracking, and wellness recommendations.
Challenges I Faced
One of the main challenges was detecting emotions accurately using rule-based NLP techniques. Designing a meaningful wellness score and clear visualizations while keeping the app fast and simple was also challenging.
What I Learned
Through this project, I learned how to apply NLP concepts in a real-world application, build and connect APIs with a frontend, and present emotional data in a way that is easy for users to understand.
Built With
- analysis
- apis
- backend
- built
- fastapi
- frameworks
- frontend
- github
- javascript
- languages
- lucide
- nltk
- other
- pages
- python
- react
- recharts
- rest
- sentiment
- textblob
- vader
- vite
- with