The goal of our project is to develop a deep neural network model for emotion classification, specifically targeting six emotions: happy, sadness, anger, love, fear, and surprise. Our aim is to build an accurate and robust model that can accurately classify emotions based on input text data. The model will be trained on a large dataset of labeled examples to learn the patterns and features associated with each emotion. The ultimate objective is to create a powerful tool that can automatically detect and classify emotions. By achieving this goal, we aim to contribute to the field of emotion recognition.
The 6 different emotions included are:
- happy
- sadness
- anger
- love
- fear
- surprise
Glimpse of dataset split between the 6 emotions:
- DNN_Basic.ipynb file has simple model without using SMOTE
- DNN_with_SMOTE.ipynb file has model run after using SMOTE method