A systematic review of figurative language detection: Methods, challenges, and multilingual perspectives

• Figurative language styles are analyzed at lexical, syntactic, semantic, discourse-level, and pragmatic NLP levels • Discourse-level styles dominate due to sarcasm’s prevalence in social media • Arabic ranks second in dataset count, but lacks stylistic diversity coverage • Embedding-based features lead performance, especially in semantic tasks • Publicly available datasets remain scarce for pragmatic and syntactic styles Figurative language detection has emerged as a critical task in natural language processing (NLP), enabling machines to comprehend non-literal expressions such as metaphor, irony, and sarcasm. This study presents a systematic literature review with a multilevel analytical framework, examining figurative language across lexical, syntactic, semantic, discourse, and pragmatic levels. We investigate the interplay between feature engineering, model architectures, and annotation strategies across different languages, analyzing datasets, linguistic resources, and evaluation metrics. Special attention is given to morphologically rich and low-resource languages, where deep learning dominates but rule-based and hybrid approaches remain relevant. Our findings indicate that deep learning models–especially transformer-based architectures like BERT and RoBERTa–consistently outperform other approaches, particularly in semantic and discourse-level tasks, due to their ability to capture context-rich and abstract patterns. However, these models often lack interpretability, raising concerns about transparency. Additional challenges include inconsistencies in annotation practices, class imbalance between figurative and literal instances, and limited data coverage for under-resourced languages. The absence of standardized evaluation metrics further complicates cross-study comparison, especially when diverse figurative language styles are involved. By structuring our analysis through linguistic and computational dimensions, this review aims to facilitate the development of more robust, inclusive, and explainable figurative language detection systems.