Cyberbullying is a serious problem that can have detrimental effects on individuals' mental health and well-being. Given the large volume of tweets generated daily, manually identifying cyberbullying is time-consuming and inefficient. This tool has been developed to address this challenge by efficiently flagging potentially harmful tweets, aiming to create a safer online environment for all.
- Used Kaggle's Cyberbullying Dataset with under-sampling.
- Cleaned and transformed data for machine learning.
- Trained various models using cross-validation.
- Built a user-friendly API with Flask.
Python Version: 3.10
Packages: numpy, pandas, nltk, scikit-learn, xgboost, flask, json, pickle
Original Dataset: https://www.kaggle.com/datasets/andrewmvd/cyberbullying-classification
Setting Up Environment:
conda create -p venv python=3.10 -ypip install -r requirements.txt
The dataset utilized, Cyberbullying Classification Dataset from Kaggle. This dataset is tailored for binary classification, distinguishing potentially harmful tweets from non-cyberbullying ones.
A Python script is employed for comprehensive text data cleaning, encompassing operations such as:
- Punctuation removal
- Numeric character removal
- Lowercasing
- Stop word elimination
- Lemmatization/Stemming
- URL removal
- Data Splitting: Segregation of data into training and testing sets with a 70-30 ratio.
- Feature Engineering: Utilization of a transformation pipeline integrating CountVectorizer and TfidfTransformer.
- Model Training and Evaluation: Training multiple models via cross-validation, with selection based on accuracy and training efficiency. The XGBClassifier model is chosen for its superior performance.
- Fine-tuning: Refinement of the XGBClassifier model for optimal performance.
After cross-validation, the models show the following performances:
The project culminates in the development of a user interface facilitated by Flask, along with an API endpoint for real-time cyberbullying detection.
