Comparative study on deep learning models in humor detection

Chunyang Wang; Shiqi Xin; Murong Yi

doi:10.1117/12.2615164

15 October 2021 Comparative study on deep learning models in humor detection

Chunyang Wang, Shiqi Xin, Murong Yi

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330L (2021) https://doi.org/10.1117/12.2615164
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

Humor detection is one of the most popular tasks in natural language processing. Yet humor is abstract to numerate, and there isn’t an acknowledged standard for humor assessment. Toward this end, this paper explores the performance of CNN, RNN, BiLSTM in tackling humor detection. We use the regression method and classification method, respectively, to identify the best model. The experiment was conducted on a dataset that is composed of 15,000 news headlines. Results show that the CNN network is a preferable choice, and combined with the classification method, the model obtains the best performance. Though there is plenty of more sophisticated sentiment analysis models, our work offers an intuition for short text sentiment analysis.

Citation Download Citation

Chunyang Wang, Shiqi Xin, and Murong Yi "Comparative study on deep learning models in humor detection", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330L (15 October 2021); https://doi.org/10.1117/12.2615164

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available