Project Description:

The AI-Based Mood Tracker from Text Input is a web application that uses Natural Language Processing (NLP) to analyze users' written journal entries, comments, or thoughts and determine their emotional state (e.g., happy, sad, anxious, angry). The system helps users understand their mental health trends over time, and can offer suggestions or visualizations based on mood history.

This project combines frontend UX design with AI-based backend logic, making it an excellent final-year project for Computer Science students interested in AI + web development.

Core Functionality:

User Input:

• Users can write their thoughts daily (like a diary entry or a status update).

• Optionally allow voice-to-text input.

 Sentiment & Emotion Detection:

• Use AI models (pre-trained or custom) to analyze text and classify emotions:

  • e.g., Joy, Sadness, Anger, Fear, Disgust, Surprise, Neutral

• Optionally detect intensity of mood (scale of 1–10).

 Mood History Dashboard:

• Users can view:

  • Weekly and monthly mood summaries.

  • Line or pie charts showing mood trends over time.

  • Word clouds of commonly used terms.

 Personalized Tips:

• Suggest content based on mood:

  • Inspirational quotes, songs, mental health tips, calming videos, etc.

 User Profile:

• Mood journaling history.

• Set personal mood goals.

• Option to download data.

Tech Stack:

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript (React or plain JS)

 Backend (any one of the following):

• PHP (with AI integration via APIs or Python bridge)

• Java (Spring Boot + NLP model REST API)

• Node.js (Express + Python NLP microservice)

 AI/NLP Integration:

• Pre-trained Models:

  • HuggingFace Transformers (e.g., BERT, RoBERTa)

  • Google Cloud NLP or IBM Watson Tone Analyzer (via API)

• Custom Models:

  • Trained using Python (NLTK, TextBlob, scikit-learn, TensorFlow, etc.)

 Database:

• MongoDB / MySQL / PostgreSQL

• Store user entries and mood tags