research-article

Automatic Algerian Sarcasm Detection from Texts and Images

Authors:

Kheira Zineb Bousmaha,

Khaoula Hamadouche,

Hadjer Djouabi,

Lamia Hadrich-BelguithAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 7

Article No.: 108, Pages 1 - 25

https://doi.org/10.1145/3670403

Published: 19 July 2024 Publication History

Abstract

In recent years, the number of Algerian Internet users has significantly increased, providing a valuable opportunity for collecting and utilizing opinions and sentiments expressed online. They now post not just texts but also images. However, to benefit from this wealth of information, it is crucial to address the challenge of sarcasm detection, which poses a limitation in sentiment analysis. Sarcasm often involves the use of nonliteral and ambiguous language, making its detection complex. To enhance the quality and relevance of sentiment analysis, it is essential to develop effective methods for sarcasm detection. By overcoming this limitation, we can fully harness the expressed online opinions and benefit from their valuable insights for a better understanding of trends and sentiments among the Algerian public. In this work, our aim is to develop a comprehensive system that addresses sarcasm detection in Algerian dialect, encompassing both text and image analysis. We propose a hybrid approach that combines linguistic characteristics and machine learning techniques for text analysis. Additionally, for image analysis, we utilized the deep learning model VGG-19 for image classification, and employed the EasyOCR technique for Arabic text extraction. By integrating these approaches, we strive to create a robust system capable of detecting sarcasm in both textual and visual content in the Algerian dialect. Our system achieved an accuracy of 92.79% for the textual models and 89.28% for the visual model.

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Cited By

Pokhriyal HJain G(2025)Supposititious Sarcasm Detection and Sentiment Analysis Coping Hindi Language in Social Networks Harnessing Zipf- Mandelbrot Probabilistic Optimisation and Perplexity Entropy LearningACM Transactions on Asian and Low-Resource Language Information Processing10.1145/371206124:2(1-28)Online publication date: 16-Jan-2025
https://dl.acm.org/doi/10.1145/3712061
Subalakshmi NBabubalaji R(2024)Improving the Accuracy of Sarcasm Detection in Text Data Using a Smooth Support Vector Classification Model with Word-Emoji Embedding for News and Indian Indigenous Languages2024 International Conference on System, Computation, Automation and Networking (ICSCAN)10.1109/ICSCAN62807.2024.10894530(1-6)Online publication date: 27-Dec-2024
https://doi.org/10.1109/ICSCAN62807.2024.10894530

Index Terms

Automatic Algerian Sarcasm Detection from Texts and Images
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 7

July 2024

254 pages

EISSN:2375-4702

DOI:10.1145/3613605

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2024

Online AM: 03 June 2024

Accepted: 24 May 2024

Revised: 06 February 2024

Received: 14 September 2023

Published in TALLIP Volume 23, Issue 7

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Cited By

Pokhriyal HJain G(2025)Supposititious Sarcasm Detection and Sentiment Analysis Coping Hindi Language in Social Networks Harnessing Zipf- Mandelbrot Probabilistic Optimisation and Perplexity Entropy LearningACM Transactions on Asian and Low-Resource Language Information Processing10.1145/371206124:2(1-28)Online publication date: 16-Jan-2025
https://dl.acm.org/doi/10.1145/3712061
Subalakshmi NBabubalaji R(2024)Improving the Accuracy of Sarcasm Detection in Text Data Using a Smooth Support Vector Classification Model with Word-Emoji Embedding for News and Indian Indigenous Languages2024 International Conference on System, Computation, Automation and Networking (ICSCAN)10.1109/ICSCAN62807.2024.10894530(1-6)Online publication date: 27-Dec-2024
https://doi.org/10.1109/ICSCAN62807.2024.10894530

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