Open Access

ARTICLE

Humor detection using deep learning in 10 years: A survey

Chengjuan Ren¹, Ziyu Guo², Ping Zhang³, Yuhan Gao^4,*

1 College of Language Intelligence, Sichuan International Studies University, Chongqing, China
2 Department of Computer Science & Engineering, The Chinese University of Hong Kong, China
3 College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
4 *Corresponding author - KIPP Northeast Denver Middle School, 4635 Walden St. Denver, Colorado 80249, USA

* Corresponding Authors: Chengjuan Ren (), Ping Zhang ()

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 2024, 40(1), 1-9. https://doi.org/10.23967/j.rimni.2024.01.006

Received 13 October 2023; Accepted 19 January 2024; Issue published 31 January 2024

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.

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Keywords

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