  Understanding various humour styles is essential for comprehending the
multifaceted nature of humour and its impact on fields such as psychology and
artificial intelligence. This understanding has revealed that humour, depending
on the style employed, can either have therapeutic or detrimental effects on an
individual's health and relationships. Although studies dedicated exclusively
to computational-based humour style analysis remain somewhat rare, an expansive
body of research thrives within related task, particularly binary humour and
sarcasm recognition. In this systematic literature review (SLR), we survey the
landscape of computational techniques applied to these related tasks and also
uncover their fundamental relevance to humour style analysis. Through this
study, we unveil common approaches, illuminate various datasets and evaluation
metrics, and effectively navigate the complex terrain of humour research. Our
efforts determine potential research gaps and outlined promising directions.
Furthermore, the SLR identifies a range of features and computational models
that can seamlessly transition from related tasks like binary humour and
sarcasm detection to invigorate humour style classification. These features
encompass incongruity, sentiment and polarity analysis, ambiguity detection,
acoustic nuances, visual cues, contextual insights, and more. The computational
models that emerge contain traditional machine learning paradigms, neural
network architectures, transformer-based models, and specialised models attuned
to the nuances of humour. Finally, the SLR provides access to existing datasets
related to humour and sarcasm, facilitating the work of future researchers.
