Semantic-Sentiment Fusion with Deep Learning: A Novel Framework for Hate Speech Detection

With the rapid growth of social media and frequent anonymous interactions, hate speech has become widespread. As users express diverse opinions in digital spaces, the need for effective detection remains crucial. To address this, we propose a framework applicable to diverse hate speech types, combining sentence-level semantic representation vectors from the pre-trained Bidirectional Encoder Representations from Transformers (BERT) with sentiment score vectors from the Linguistic Inquiry and Word Count (LIWC) dictionary and the Valence Aware Dictionary for sEntiment Reasoning (VADER). This semantic-sentiment fusion integrates three deep learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Deep Neural Network (DNN) to enhance detection effectiveness. To verify generalizability, we used four datasets: two binary hate speech detection tasks, two multi-class tasks, and validation on another domain dataset. Results show that the proposed framework achieved the best performance, with accuracy up to 91.34%. This approach provides valuable direction for future research.