EmotiAI – AI-Powered Text Emotion Detection
EmotiAI is a machine-learning-powered web application that analyzes English text and detects one of six emotions: sadness, joy, love, anger, fear, and surprise.
Project overview
What this project is about.
EmotiAI is an AI-powered text emotion detection system that analyzes English text and identifies the emotion expressed in the input.
The system uses the DAIR.AI Emotion dataset and a machine-learning pipeline based on TF-IDF feature extraction and a Linear Support Vector Machine (Linear SVM) classifier. The text is cleaned, converted into numerical TF-IDF features, and then classified into one of six emotion categories: sadness, joy, love, anger, fear, or surprise.
The application provides an interactive web interface where users can enter text and receive an emotion prediction. Along with the predicted emotion, EmotiAI provides a visual representation of the model's decision scores across all six emotion classes.
The system also maintains prediction history and provides application-level statistics such as the total number of predictions, emotion distribution, and most frequently detected emotion.
A dedicated contact system allows visitors to submit their name, mobile number, email address, and message. Contact submissions are stored in the application's database and can be managed through the Django administration panel.
The website includes configurable social and contact links for Instagram, LinkedIn, and email, which can be managed through the administration panel.
EmotiAI is designed for English text emotion classification and supports six emotion categories.
Who it is for
Usage and use case
- 1. Visit the EmotiAI website.
- 2. Navigate to the Model page.
- 3. Enter English text into the emotion analysis text box.
- 4. Click "Analyze Emotion".
- 5. The application sends the text to the Django backend.
- 6. The text is cleaned and transformed using the trained TF-IDF vectorizer.
- 7. The Linear SVM model predicts one of six emotions.
- 8. The detected emotion is displayed with an explanation.
- 9. A visual decision-score chart displays the model's scores for all six emotions.
- 10. The prediction is stored in the database.
- 11. Recent predictions can be viewed in the prediction history.
- 12. Application statistics are updated automatically.
- 13. Visitors can use the Contact page to submit their details and message.
- 14. Administrators can manage predictions, contact messages, and footer social/contact links through Django Admin.
Built with
Technology stack
- Python
- Django
- HTML5
- CSS3
- JavaScript
- scikit-learn
- TF-IDF
- Linear SVM
- LinearSVC
- Joblib
- SQLite
- Django ORM
- Django Admin
- Django REST-style JSON API
- Git
What you get
Key features
- AI-powered text emotion detection
- Detection of six emotions
- English text classification
- TF-IDF text feature extraction
- Linear SVM emotion classifier
- Real-time text analysis
- Visual model decision-score representation
- Prediction result with emotion-specific explanation
- Prediction history
- Application prediction statistics
- Responsive multi-page web interface
- Modern AI-focused user interface
- Home, About, Model, and Contact pages
- Database-backed contact form
- Django Admin management
- Admin-configurable Instagram, LinkedIn, and email links
- Shared responsive navbar and footer
- Client-side character counter
- Loading and error states
- Django-based backend API
- SQLite database integration
Before you start
Requirements
Python 3.13 or compatible Python 3.x version
Django 5.x
scikit-learn
joblib
SQLite
Modern web browser
Virtual environment recommended
Optional extras
Boost your project
Optional extras to help you document, present and understand your work. Mention the ones you like when you enquire.
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Popular Documentation support Submission-ready project report. - Full project report (PDF and Word)
- Setup guide and README
- Diagrams and screenshots
-
Presentation support Slides ready for your review. - Slide deck of 12 to 15 slides
- Speaker notes
- Demo walkthrough script
-
Publication support Guidance to turn it into a paper. - Paper structure and abstract
- Journal and format guidance
- Reference and plagiarism-check help
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1:1 mentorship Live help from a mentor. - 45-minute video call
- Code walkthrough
- Viva question practice
How do I get a project?
Open the project you like and use the button on its page. If online payment is available you can pay and choose how you want to receive it. Otherwise send us a short enquiry and we will reply on WhatsApp or email.
Can a project be changed to fit my requirements?
Yes. Describe the changes you need when you enquire, and we confirm the scope and the price before any work starts.
Are add-ons available?
Many projects have optional add-ons such as documentation or presentation support. They are listed on the project page with their prices, and you can pick them when you buy or enquire.
How will I receive my order?
You choose email, WhatsApp or a Google Drive link. The delivery time is shown on the project page and in your confirmation email.
Is there a demo?
Projects with a demo video have a play button on their page. If you want to see something specific, ask us and we will tell you what we can show.
Can I get a refund?
Projects are digital, so payments are generally not refundable. If something went wrong, send a refund request with your order reference and we review it fairly. The Refund Policy explains the details.
