Comparative Analysis of Hate Speech Detection Using Various Vectorization and Classification Techniques

Hate speech is a prominent, growing problem within our society, and this problem and its effects have only increased with time. According to the Center for Technology and Society, 52% of people reported being harassed online between 2022 and 2023, an increase from 40% in 2022. The new technological advancements make it easier than ever to spread negative, discriminatory, and pejorative comments to attack others with the click of a button. This is unfortunately an issue that affects many and often leads to violence, which is why it is important to identify and remove this online hate speech in order to minimize the spread. In this study, we explore the classification of online hate speech by comparing different vectorization and classification methods. We developed a set of eight classification techniques for identifying hate from a Twitter dataset containing hate speech and benign text examples. We cleaned and vectorized it in two different ways. Each of these sets is used to train deep learning, SVM, SGD, and random forest classifiers. All these methods achieved accuracy above 93%. The machine learning methods (SVM, SGD, and random forest) all achieved a nearperfect classification accuracy of 98.72%, with slight variations in precision and recall. The deep learning approach also showed improved performance with TF-IDF vectorization. Overall, the choice of vectorization had minimal impact on accuracy, and all models performed consistently well.