Emojifier

Overview

Emojifier is a deep learning-based Natural Language Processing (NLP) model designed to add relevant emoji representations to text input. It utilizes word embeddings and Long Short-Term Memory (LSTM) networks to classify sentences into predefined emoji categories.

Features

• Implements GloVe word embeddings for sentence representation.
• Uses LSTM layers for sequence modeling.
• Supports emoji classification based on textual input.
• Leverages pre-trained word embeddings to improve model accuracy.
• Employs softmax activation for classification.

Project Structure

Emojifier/
│── Emoji_v3a.ipynb      # Jupyter Notebook containing model training and evaluation
│── utils.py             # Utility functions for processing and modeling
│── emo_utils.py         # Additional helper functions 
│── test_utils.py        # Test functions for validation
│── generateTestCases.py        # Test case generation functions
│── data       # Folder containing data
│── test_utils.py        # Folder containing images of figures used in the notebook

Dependencies

Ensure you have the following Python libraries installed:

pip install numpy tensorflow emoji matplotlib

Model Architecture

The Emojifier model follows this architecture:

1. Embedding Layer - Pre-trained GloVe embeddings for word representation.
2. LSTM Layer (128 units) - Captures contextual dependencies in text.
3. Dropout Layer (0.5) - Reduces overfitting.
4. Second LSTM Layer (128 units) - Further learns sentence patterns.
5. Dropout Layer (0.5) - Ensures better generalization.
6. Dense Layer (5 units) - Outputs class scores.
7. Softmax Activation - Converts scores to probabilities.

Training

The model is trained using:

• Cross-entropy loss for classification.
• Stochastic Gradient Descent (SGD) optimizer.
• One-hot encoding for labels.
• 400 iterations for optimization.

Usage

To run the model, follow these steps:

1. Load the dataset and word embeddings.
2. Train the model using the model() function.
3. Test the model using predict().
4. Use Emojify_V2() to classify new sentences.

Future Enhancements

• Extend the emoji classification to a larger set.
• Implement attention mechanisms for better context understanding.
• Deploy as a web API for integration into messaging applications.

License

This project is open-source and available under the MIT License.

---

Author: Chirag N Vijay