Emotion Detector

A Flask-based web application that analyzes text input and detects the underlying emotional tone. The application identifies five core emotions—anger, disgust, fear, joy, and sadness—and determines which emotion is most dominant in the given text.

⚠️ Note: This application uses IBM Watson NLP services that are only accessible from the IBM Skills Network Cloud IDE environment provided in the Coursera course.

Features

• Real-time emotion analysis: Submit any text and receive instant emotion scores
• Multi-emotion detection: Analyzes text across five emotional dimensions
• Dominant emotion identification: Automatically highlights the strongest detected emotion
• Web interface: Clean, user-friendly UI for text input and results display
• REST API: Programmatic access via the /emotionDetector endpoint
• Input validation: Graceful handling of invalid or empty text submissions

Tech Stack

• Backend: Python, Flask
• Frontend: HTML, JavaScript
• NLP: Watson NLP / Embedded AI for emotion analysis

Project Structure

emotion-detector/
├── EmotionDetection/
│   ├── __init__.py
│   └── emotion_detection.py    # Core emotion detection logic
├── static/
│   └── mywebscript.js          # Frontend JavaScript
├── templates/
│   └── index.html              # Web interface template
├── server.py                   # Flask application entry point
├── test_emotion_detection.py   # Unit tests
├── LICENSE
└── README.md

Installation

1. Clone the repository

   git clone https://github.com/ajitagupta/emotion-detector.git
   cd emotion-detector

2. Create and activate a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install flask requests

Usage

Running the Application

Start the Flask server:

python server.py

The application will be available at http://localhost:5000.

Web Interface

1. Open your browser and navigate to http://localhost:5000
2. Enter the text you want to analyze in the input field
3. Click the analyze button to see the emotion breakdown

API Endpoint

Send a GET request to the /emotionDetector endpoint:

curl "http://localhost:5000/emotionDetector?textToAnalyze=I%20am%20so%20happy%20today"

Response format:

For the given statement, the system response is 'anger': 0.01, 'disgust': 0.02, 
'fear': 0.01, 'joy': 0.95, 'sadness': 0.01. The dominant emotion is joy.

Running Tests

Execute the unit test suite:

python -m unittest test_emotion_detection

Emotions Detected

Emotion	Description
Anger	Frustration, irritation, or hostility
Disgust	Revulsion or strong disapproval
Fear	Anxiety, worry, or apprehension
Joy	Happiness, contentment, or excitement
Sadness	Sorrow, grief, or disappointment

Course Context

This project was developed as the final project for the IBM AI Engineering Professional Certificate on Coursera, specifically for the course on developing AI applications with Python and Flask.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Author

Ajita Gupta

• GitHub: @ajitagupta
• Website: ajitagupta.com

Acknowledgments

• IBM Developer Skills Network for the course template and guidance
• Watson NLP for emotion detection capabilities