This project is designed to help users understand and classify emotions from textual input. The dataset is sourced from GoEmotions, a comprehensive collection of over 50,000 labeled sentences developed by Google and Stanford. The labels are 28 emotion classes, including joy, remorse, curiosity, etc. Each sentence was annotated by three English-speaking crowdworkers to ensure high-quality emotion classification.
Model Architecture:
The project delivers two distinct model implementations:
- A CNN-based architecture
- An RNN (LSTM)-based architecture
Comparative analysis revealed that the CNN model outperformed in training efficiency and demonstrated more robust generalization on unseen data, yielding more reliable predictions for new inputs.
Evaluation & Feedback:
Initially released as a free trial at the University of Toronto, the model achieved an 85% user satisfaction rate among students, who found the emotion classifications accurate and insightful.
To launch Gradio-based demo web application:
cd source
python UI.pyThis will start a local server and provide a URL, where you can interact with the emotion classification demo.
The input sentences undergo a pre-trained encoder-based transformer network to generate context-aware word embeddings. These embeddings are processed in parallel through two CNN branches with distinct kernel sizes: a 2-word convolutional filter and a 4-word convolutional filter. Each CNN pathway incorporates standard regularization techniques including batch normalization and dropout layers to prevent overfitting. The feature maps from both convolutional branches are concatenated into a unified representation vector, which is then fed through a fully connected classification network. Notably, the output layer employs sigmoid activation instead of softmax to enable multi-label classification capability.
The system was publicly released to University of Toronto students for real-world testing. Participants were invited to:
- Submit arbitrary text inputs to the model
- Review the generated emotion classifications
- Respond with the satisfaction level of the generated result.
From over 300 collected trial responses, the model demonstrated over 85% success rate in accurate emotion recognition.
Sample model inference that demonstrate the performance of the model. Inputs are randomly picked from r/uoft comments.
