A smart journaling web application that empowers users to understand, track, and reflect on their emotions using AI-powered sentiment and aspect analysis.
Note Mood is a graduation project featured in Al-Watan newspaper 🎓.
It aims to foster emotional awareness and mental well-being by analyzing users’ daily journals using advanced machine learning techniques.
Users can write personal journals, and our system processes these entries to:
- Analyze the overall mood (positive, negative, neutral).
- Extract aspects (e.g., "Work", "Family", "Health") and identify the sentiment toward each.
- Identify the most frequent topics in their life.
- Visualize emotional trends over time in a meaningful way.
🎥 You can watch a short video demo of the app here
| Login | New Journal | Journals |
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| Help | category | Charts |
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- Sentiment Analysis Model analyzes the mood of each journal entry.
- Attention-based Aspect Extraction Model detects specific entities and topics discussed.
- Each aspect is then analyzed for individual sentiment.
- Comprehensive charts and reports are generated to help users understand emotional triggers.
- ✍️ Write and store daily journals.
- 🔍 Search through journal entries.
- 📈 View emotional trends with visual charts.
- 📊 Aspect-based sentiment analysis.
- 📚 Topic extraction from journals.
- 🧾 Daily and overall emotion summaries.
- Frontend: React.js
- Backend: ASP.NET Core + Entity Framework
- Database: SQL Server (Azure-hosted)
- Machine Learning: Sentiment & Aspect Analysis Models
- Deployment: Azure + GitHub Actions
- Other tools: Hangfire, Lucene Search, JWT Authentication
All documentation including system architecture, dataset details, and ML model explanation is available here:
📁 Documentation Drive Folder
Fron End deployed on vercel ,The backend hosting was deployed on a free tier which has now expired. However, you can view detailed screenshots and the full design experience above.