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

作     者：Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen 

作者机构：Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied LinguisticsCollege of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of EnglishCollege of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer SciencesCollege of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information SystemsCollege of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of ComputerDeanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language PreparationArabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer ScienceFaculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第5期

页      面：4137-4154页

核心收录：

学科分类：0710[理学-生物学] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43) Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR33) 

主　　题：Computational linguistics natural language processing deep learning irony detection social media 

摘      要：Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** 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 *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.