FROM DETECTION TO PRECISION: ELEVATING HATE SPEECH CLASSIFICATION WITH CUTTING-EDGE MODELS

Abstract

In the digital era, the proliferation of hate speech on social media platforms has necessitated the development of effective detection systems. This paper presents a comprehensive comparative analysis of machine learning and deep learning approaches for hate speech classification across diverse datasets, including a thorough comparison with existing methodologies. Specifically, this study evaluates the performance of two machine learning models Random Forest and XGBoost and two deep learning models, LSTM and BERT. Each model is trained using various embeddings, including Word2Vec, as well as GloVe, supplemented by TF-IDF for the machine learning models. Through rigorous cross-validation and hyperparameter tuning, the efficacy of each model and embedding combination is assessed. The results are analyzed not only to determine the most effective approach for hate speech detection but also to benchmark these results against previous studies in the field. This comparative analysis provides insights into the strengths and limitations of the models and embeddings used, aiming to contribute to the ongoing efforts in creating a safer online environment by advancing the state-of-the-art in hate speech detection.