LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

作     者：Azlinah Mohamed Zuhaira Muhammad Zain Hadil Shaiba Nazik Alturki Ghadah Aldehim Sapiah Sakri Saiful Farik Mat Yatin Jasni Mohamad Zain 

作者机构：Institute for Big Data Analytics and Artificial IntelligenceUniversiti Teknologi MARA(UiTM)Shah AlamMalaysia College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARA(UiTM)Shah AlamMalaysia 

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

年 卷 期：2023年第75卷第4期

页      面：1577-1601页

核心收录：

学科分类：12[管理学] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Research Groups Program Grant no.(RGP-1443-0045) 

主　　题：Sentiment analysis sentiment lexicon machine learning imbalanced data deep learning method unemployment rate 

摘      要：The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic *** analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social ***,limited research has been conducted in this domain using theLexDeep *** study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 ***,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)***,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the *** performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and *** resultsdemonstrated that all LexDeep models with simple embedding outperformedthe *** confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific *** terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe *** research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.