This project aims to develop a cyberbullying tweet recognition system using machine learning techniques. The project includes data preprocessing, model building, and a user-friendly web application built using Streamlit.

• Data preprocessing including text cleaning, tokenization, stemming, and lemmatization.
• Model training using Linear Support Vector Machine (LSVM) for cyberbullying tweet detection.
• Streamlit web application for user interface.
• Prediction of cyberbullying content based on user input.

1. Clone the repository:

   git clone https://github.com/srishrachamalla7/cyberbullying-recognition.git
   cd cyberbullying-recognition

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

• data/: Contains the dataset used for training and testing.
• models/: Includes saved model files after training.
• notebooks/: Jupyter notebooks for data analysis and preprocessing.
• app.py: Streamlit web application for user interaction.
• train_model.py: Script for model training.
• preprocess.py: Functions for data preprocessing.
• utils.py: Utility functions used across the project.

1. Run the Streamlit app using the command mentioned above.
2. Input a tweet in the app.
3. The app predicts whether the input tweet contains cyberbullying content or not.

• Enhance model performance by experimenting with different algorithms and hyperparameters.
• Include more advanced text processing techniques.
• Extend the web app with more interactive features and visualizations.

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