Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Detecting sarcastic expressions with deep neural networks

Zihang Huang * 1
1 Shenzhen College of International Education, Shenzhen, China

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 62-68
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Zihang Huang. Detecting sarcastic expressions with deep neural networks. ACE (2023) Vol. 5: 62-68. DOI: 10.54254/2755-2721/5/20230529.

Abstract

Following the ever increasing trend in social media such as Twitter, Facebook, and Instagram, automatic analysis of people’s conversations and languages have become a problem of great significance for businesses and governments in attempt to understand and analyze people’s habits, thoughts, and patterns towards different subjects of interests. Within the field of natural language processing, sarcasm detection has always been a difficult challenge for sentiment analysis. Recent years, there has been great interests shown by researchers towards sarcasm detection. Neural networks achieve huge success and advancements surrounding this topic, but reviews for this task are very limited and there’s a lack of comprehensive review of the development of sarcasm detection so far. Thus, this paper aims to summarize and present the various methods directed towards sarcasm detection, the progress it has made, and examination of potential problems and availability for further improvements.

Keywords

sarcasm detection, deep learning

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230529
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated