Cyberbullying Detection App (English) with Streamlit GUI

Overview

A Cyberbullying Detection App designed to identify instances of cyberbullying in English text. The app incorporates a diverse set of machine learning and deep learning algorithms, including BERT, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), and others.

Key Features

• Algorithmic Diversity: A blend of traditional machine learning algorithms and cutting-edge deep learning models for robust cyberbullying detection.
• Preprocessing: Stringent text preprocessing involving tokenization, stemming/lemmatization, and removal of stop words, ensuring high-quality data for model training and assessment.
• Feature Representation: Varied feature representations such as bag-of-words, TF-IDF, and embeddings cater to different algorithmic requirements, capturing semantic connections within the text.
• Model Performance: A detailed classification report for the English dataset showcasing accuracy, precision, recall, and F1-score metrics for each algorithm.

Dataset

The English dataset for this cyberbullying detection app employs multi-class classification, categorizing texts into four bullying categories (gender-based, age-related, religious, and ethnic) along with a "Not Bullying" class.

Bullying Classifications:

1. Gender-based Bullying: Degrading words related to gender.
2. Age-related Bullying: Occurs in adolescent environments.
3. Religious Bullying: Discrimination or derision based on beliefs.
4. Ethnic-based Bullying: Prejudice or racial insults.

Classification Report

Algorithm	Accuracy	Precision	Recall	F1-Score
BERT	0.95	0.95	0.95	0.95
RF	0.93	0.93	0.93	0.93
SVM	0.92	0.92	0.92	0.92
DT	0.93	0.92	0.93	0.92
MLP	0.90	0.90	0.90	0.90
LR	0.92	0.92	0.92	0.92

Live Site

You can also access the live version of the app on Click Here.

Streamlit App in Action (Results)

Cyberbullying

Not Cyberbullying

Sample Texts

1. "It's always the filthy bitch that creates a problem between us."
2. "Do you believe it is appropriate to refer to a Muslim as a terrorist?"
3. "I hope you're doing well and having a great day. Let's catch up soon! 😊"
4. "The team's score is disgraceful."