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Open Access

ARTICLE

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

Choongwon Kang1,2, Haein Lee3,4, Jang Hyun Kim1,2,*
1 Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
2 Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, Republic of Korea
3 School of Interdisciplinary Studies, Dongguk University, Seoul, Republic of Korea
4 Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, Republic of Korea
* Corresponding Author: Jang Hyun Kim. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.078997

Received 12 January 2026; Accepted 18 March 2026; Published online 13 April 2026

Abstract

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.

Keywords

Hate speech detection; natural language processing (NLP); deep learning; sentiment analysis

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