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 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第5期4137-4154,共18页计算机、材料和连续体（英文）

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).

摘　　要：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 betterway.Irony and sarcasm detection is a complex task inNatural 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 presentedCLODBN-IRCmethod 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.

关 键 词：Computational linguistics natural language processing deep learning irony detection social media 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]