Cyberbullying Tweet Recognition Project

Introduction

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.

Features

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.

Getting Started

Clone the repository:

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

Install the required dependencies:
```
pip install -r requirements.txt
```
Run the Streamlit app:
```
streamlit run app.py
```

Project Structure

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.

Usage

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

Future Scope

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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