A Comprehensive Review of Sentiment Analysis Techniques for Cyberbullying and Hate Speech Detection

Abstract

ABSTRACT-This paper presents a comprehensive review of sentiment analysis techniques applied to hate speech and cyberbullying detection, covering lexicon-based methods, traditional machine learning models, deep learning architectures, hybrid systems, and ensemble approaches. The review emphasizes multilingual and code-mixed scenarios, sarcasm detection, data imbalance handling. Relevant studies published were collected from major scientific databases, including IEEE Xplore, ACM Digital Library, Springer, Elsevier, and Google Scholar, using carefully selected keywords related to hate speech, cyberbullying, multilingual NLP, transformers, and ensemble learning. Only peer-reviewed and empirically validated studies were considered in this review.The analysis reveals that transformer-based and ensemble models consistently outperform single-model architectures, particularly in multilingual and low-resource settings. Hybrid frameworks that integrate lexical knowledge with deep contextual representations demonstrate improved robustness and generalization. However, the literature also highlights persistent limitations, including weak handling of sarcasm and implicit hate, high sensitivity to dataset bias, and insufficient focus on interpretability and fairness.Based on the identified gaps, this review discusses future research directions, including the development of explainable hybrid ensembles, improved sarcasm-aware architectures, dynamic lexicon integration, and cross-domain multilingual evaluation. The findings of this review aim to guide researchers toward building more robust, ethical, and generalizable hate speech and cyberbullying detection systems for real-world applications.