Open Access

ARTICLE

Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Abdulkhaleq Q. A. Hassan³, Abdulbaset Gaddah⁴, Nasser Allheeib⁵, Suleiman Ali Alsaif⁶, Badriyya B. Al-onazi⁷, Heba Mohsen⁸

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of English, College of Science and Arts at Mahayil, King Khalid University, Abha, 62217, Saudi Arabia
4 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Makkah, 24211, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
6 Department of Computer, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P. O. Box 1982, Dammam, 31441, Saudi Arabia
7 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
8 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2023, 75(2), 4137-4154. https://doi.org/10.32604/cmc.2023.035237

Received 13 August 2022; Accepted 14 October 2022; Issue published 31 March 2023

Abstract

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content. Therefore, this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification (CLODBN-IRC) model on social media. The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media. To attain this, the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction. For irony detection and classification, the DBN model is exploited in this work. At last, the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization (IABC) algorithm. The experimental validation of the presented CLODBN-IRC method can be tested by making use of benchmark dataset. The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

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Keywords

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