ABSTRACT Understanding sarcasm in online communication is crucial for accurately gauging public opinion. Since sarcasm conveys the opposite of its literal meaning, it throws a wrench into automated sentiment analysis tools. This has fuelled the development of new methods to identify sarcasm in text and speech. Researchers are turning to advanced techniques like word embedding (Word2Vec), Transformers (BERT) and deep learning models (RNNs) to improve the accuracy of sarcasm detection systems. Sarcasm prevalence in social media poses a challenge to sentiment analysis accuracy. This review investigates recent advancements in sarcasm detection using machine learning and deep learning techniques. We analyse studies published between 2018 and 2023, identified through a comprehensive search of Google Scholar, ScienceDirect, SpringerLink, ResearchGate and Semantic Scholar. Following PRISMA guidelines, 37 studies were selected based on inclusion/exclusion criteria. Our analysis explores four research questions and summarises the utilised datasets, algorithms and the significance of multi‐modality and context in sarcasm detection. The review concludes that deep learning approaches achieve superior performance compared with other methods. In conclusion, we found that making use of multimodality significantly enhances the performance of models in sarcasm detection. The multi‐modality model using the deep learning technique Bidirectional Gated Recurrent Unit (BiGRU) achieved the highest performance with an accuracy of 99.1%. Furthermore, it highlights the potential of multi‐modal integration for enhanced accuracy in sarcasm detection.
Sharma et al. (2026) studied this question.