Text-Emotion-Classification-NLP

Overview

This repository presents a comprehensive approach to emotion classification on text data labeled with six emotions:

• Sadness
• Joy
• Love
• Anger
• Fear
• Surprise

We explore and compare four deep learning architectures, each in its own notebook:

1. CNN (cnn.ipynb)

   • Embedding layer, convolutional filters, and global max pooling
   • Achieves around 90% accuracy (depending on hyperparameters)

2. LSTM (lstm.ipynb)

   • Incorporates LSTM units to capture sequential text patterns
   • Dropout for regularization and optional RMSprop/Adam optimizers

3. BiGRU (BiGRU.ipynb)

   • Bidirectional GRU capturing context in both directions
   • Approx. 94% accuracy with extensive preprocessing (emoji handling, chat word cleanup)

4. BERT (Bert.ipynb)

   • Fine-tunes a pre-trained BERT model
   • Often yields the highest accuracy (~97%) but requires more computational resources

Dataset

The dataset is sourced from:
“Emotion dataset.” Kaggle: NIDULA ELGIRIYEWITHANA. https://www.kaggle.com/datasets/nelgiriyewithana/emotions (accessed Apr. 2, 2024).

• Approximately 400,000 tweets labeled with one of the six emotions.
• Preprocessing includes tokenization, stopword removal, padding, and optional class balancing.

Key Features

• Data Preprocessing: Includes removing emojis/chat words, balancing classes, tokenizing, and padding.
• Model Development: Each notebook details training setup, hyperparameters (batch size, learning rate, epochs), and the chosen optimizer.
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, Confusion Matrix, and sometimes ROC curves.
• Comparison: Highlights how model complexity and architecture choice affect classification performance.

Project Structure

.
├── cnn.ipynb           # CNN-based text classification
├── lstm.ipynb          # LSTM-based approach
├── BiGRU.ipynb         # Bidirectional GRU
├── Bert.ipynb          # BERT-based model
├── text.csv                # Contains Kaggle Emotion dataset 
└── README.md           # Project documentation

Installation & Setup

1. Clone this repository:

   git clone https://github.com/YourUsername/Text-Emotion-Classification-NLP.git
   cd Text-Emotion-Classification-NLP

2. Obtain and place the Kaggle Emotion dataset:
   • Download from Kaggle: NIDULA ELGIRIYEWITHANA’s Emotion dataset.

Usage

1. Launch Jupyter Notebook (or JupyterLab):

   jupyter notebook

2. Open any of the notebooks (e.g., cnn.ipynb, lstm.ipynb, etc.):

   • Run the cells in order to load data, build the model, and evaluate results.
   • Each notebook details its own architecture and training routine.

3. Compare Results:

   • Look at accuracy, F1 scores, and confusion matrices to see how CNN, LSTM, BiGRU, and BERT differ in performance.

Results

• CNN: About 90% accuracy, excels at extracting local features from text.
• LSTM: Captures long-term context, around the low- to mid-90s accuracy.
• BiGRU: Often surpasses LSTM in capturing bidirectional context, ~94% accuracy.
• BERT: Typically the best performer (~97% accuracy), though more computationally intensive.

License

This project is licensed under the MIT License.