Unmasking Toxicity: A Comprehensive Analysis of Hate Speech Detection in Banglish

As the digital landscape expands, the rise of online hate speech presents a pressing challenge, necessitating sophis-ticated tools for effective detection and mitigation. This project focuses on the intricate linguistic landscape of Banglish a hybrid language amalgamating Bengali and English striving to develop robust models tailored to its unique characteristics. The dataset, comprising 5000 Banglish comments categorized into various hate speech types, serves as the foundation for model exploration. Our approach spans a wide variety of models, including traditional machine learning (SVM, Logistic Regression,random forest), advanced deep learning architectures and innovative hybrid models (CNN+BiLSTM). Approaches for feature extraction such word embedding, TF-IDF, and Bag-of-Words and sentiment analysis scores are adapted to the nuances of Banglish. Ethical considerations guide our development, addressing algorithmic bias and user rights. The experimental results provide a nuanced understanding of model performance, in- cluding accuracy (90%), precision, recall, and F1 score. Insights derived from these analyses contribute to the ongoing refinement of hate speech detection methodologies, advancing the field of computational linguistics and ethical artificial intelligence.