Text Emotion Classification

This project classifies text into one of four emotions: Joy, Sadness, Anger, or Fear. The system uses a Recurrent Neural Network (RNN) model for high accuracy and integrates with a Streamlit application for real-time emotion prediction.

Project Overview

1. Data Preparation

• Data Collection:

  • The dataset used is collected from Kaggle. You can find it here.

• Data Cleaning and Filtering:

  • Removed duplicate rows.

• Text Preprocessing:

  • Cleaned texts by removing non-alphabetic characters, converting to lowercase, removing stopwords, and applying stemming.

• Feature Extraction:

  • Trained a Word2Vec model to convert text into vector representations.
  • Computed average word vectors for each text sample.

• Dataset Splitting:

  • Split data into training and validation sets.
  • Encoded labels for classification.

2. Model Training

• Gradient Boosting Models:

  • Used Optuna for hyperparameter tuning of LGBMClassifier and XGBClassifier.
  • Evaluated cross-validation scores to select the best model.

• RNN Model:

  • Built a Sequential model with an Embedding layer, SimpleRNN layer, and Dense output layer.
  • Achieved an accuracy of approximately 89% with this RNN model, which significantly outperforms traditional machine learning models that achieve 61-64% accuracy.

• Gated RNN Model:

  • Enhanced the model by replacing the SimpleRNN layer with a GRU (Gated Recurrent Unit) layer.
  • This improvement boosted the accuracy to 93.5%, demonstrating a clear enhancement over the standard RNN.

• Model and Tokenizer Saving:

  • Saved the trained GRU model, tokenizer, and label encoder for future use.

3. Streamlit Application

A Streamlit application is developed to allow users to input text and receive emotion predictions in real-time. For more details on running the Streamlit application, refer to the following instructions:

1. Install Dependencies:

   • Ensure you have the required libraries installed:

     pip install streamlit pandas numpy pickle5 nltk keras tensorflow

2. Run the Streamlit App:

   • Navigate to the directory containing your Streamlit script and run:

     streamlit run your_script_name.py

3. Interact with the App:

   • Open the provided URL in your browser to use the text classification app.

Additional Notes

• Ensure that the maxlen parameter in the Streamlit app matches the value used during model training.
• The model and tokenizer files (emotion_classification_rnn.h5, tokenizer.pkl, and label_encoder.pkl) should be in the same directory as the Streamlit app or provide the correct path.