Emotion Prediction Model

Overview

This project aims to predict the emotion conveyed in a given text using a deep learning model. The model is trained on a dataset containing 400,000 text samples labeled with six emotions: sad, anger, surprise, love, joy, and fear.

Technologies

• Python
• NLTK
• TensorFlow
• Flask

Steps

1. Data Collection: The data used for this project is taken from Kaggle.
2. Data Preprocessing: Many preprocessing steps are applied on the data to prepare it for training.
3. Model Architecture:
   • Built a deep learning model having an Embedding layer followed by 4 Bidirectional GRU layers having 2M parameters.
   • Dropout and BatchNormalization layers were also used to prevent overfitting and for regularization.
   • In the last Flatten layer followed by two FC(FullyConnected) layers were used to get the prediction
4. Training: Trained the model on the prepared data and used techniques like learning rate scheduling and early stopping.
5. Evaluation: Evaluated the model's performance on a separate validation set and achieved 94% accuracy.
6. Deployment: Deployd the trained model as a web app using Flask.
7. Usage: Use the web application to input text and predict the corresponding emotion.

Techniques

• Cleaning Text: Remove any unwanted words from the text(eg. stopwords).
• Tokenization: Convert text into tokens for model input.
• Padding: Padd the text to have a same length for every inputs.
• Deep Learning: Use a deep learning model for emotion prediction.
• Flask: Build a web application for interacting with the model.

Outcomes

• Trained a deep learning model to predict emotions in text.
• Deployed the model as a web application for easy usage.
• This model can be used in various applications such as sentiment analysis in customer reviews, emotion detection in social media posts, and personal assistant applications to understand user emotions.

Usage

1. Install the required dependencies using pip install -r requirements.txt.
2. Download the pre-trained model weights and place them in the models/ directory.
3. Run the Flask web application using python app.py.
4. Access the application in your web browser at http://localhost:5000.

Web App