Abstract

Humor is an important part of personal communication. How to create a computational model to recognize humor is still a very challenging task in natural language processing and linguistics. In this survey, we applied some rules to leave 29 articles spanning 10 years (2012 to 2023). The main elements covered by this survey include: recent state-of-the-art detection methods using deep learning from years 2012-2023, (2) summarizing features for humor detection from a linguistic perspective, (3) humor detection datasets, evaluation metrics, data domains and languages, (4) some tricks used in humor detection (e.g. Attention mechanism, multimodal), (5) recognizing open problems and highlight the feasible opportunities for future research directions. To the best of our knowledge, this is the first systematic survey for humor detection using deep learning. The survey can be used to assist novice and prominent researchers to understand the concept of humor, popular method and future research direction and so on.