Hate Speech Detection Using LSTM and Machine Learning Models

The hate speech detection system in the paper employs machine learning and deep learning to improve performance accordingly on aspects such as classification accuracy and generalization. Some of the tried machine learning models include Logistic Regression, SGD Classifier, Decision Tree Classifier, Random Forest Classifier, and XGB Classifier, whose metrics are accuracy, precision, recall, F1-score, and the highest performance measured was 88% for the Random Forest Classifier because of ensemble learning itself. A deep learning model using the architecture of the Bidirectional LSTM has been implemented with embedding layers to handle semantic representation, and a Bidirectional LSTM layer captures the contextual relations of the text data. The proposed model was optimized through RMS Prop, using categorical cross-entropy loss, is robustly trained in terms of accuracy and loss curves, has smooth convergence without any trace of overfitting, hence ensuring good generalization on unseen data. The accuracy was further supported by using an evaluation consisting of a confusion matrix, test predictions, where two most important metrics were well classifying between "None" and "Offensive and Hate Speech." Accordingly, the system strikes an excellent balance between model complexity and interpretability, then applies to content moderation, policy enforcement across various social and online platforms.