Moodify: Emotion Analysis through Deep Learning
This innovative project uses the latest machine learning techniques to detect and classify emotions from text data. By leveraging neural network architectures such as LSTM and BiLSTM, we have developed a robust solution for understanding emotions in text.
- Develop an intelligent emotion classification system
- Analyze text data using state-of-the-art deep learning models
- Provide insights into emotional context of text
Our dataset comprises Twitter messages meticulously labeled with five primary emotional categories:
| Emotion | Description |
|---|---|
| 馃槩 Sadness | Expressions of grief, sorrow, or melancholy |
| 馃槃 Happiness | Texts conveying joy, pleasure, or excitement |
| 馃槧 Anger | Messages expressing frustration or irritation |
| 馃槹 Worry | Texts indicating anxiety or concern |
| 馃帀 Enthusiasm | Energetic and passionate communications |
Dataset Source: Emotion Classification Dataset
- Advanced emotion classification using deep learning
- Comprehensive text preprocessing and cleaning
- Balanced dataset through intelligent downsampling
- Detailed visualizations of word frequencies
- Flexible and trainable neural network models
- TensorFlow
- Matplotlib
- NumPy
- Seaborn
- NLTK
- WordCloud
- Scikit-learn
- Embedding Layer: Transforms text into dense vector representations
- Dropout Layer: Prevents overfitting
- LSTM Layer: Captures sequential dependencies
- Dense Layer: Emotion classification with softmax activation
- Embedding Layer: Text vectorization
- Spatial Dropout: Advanced regularization
- Bi-directional LSTM: Captures context from both directions
- Dense Layer: Multi-class emotion prediction
| Model | Training Accuracy | Validation Accuracy |
|---|---|---|
| LSTM | 92.12% | 89.99% |
| BiLSTM | 93.67% | 90.68% |
- Expand emotion categories
- Implement transfer learning