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

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Computers, materials & Continua 2023年第4期75卷 1577-1601页

作者： Azlinah Mohamed Zuhaira Muhammad Zain Hadil Shaiba Nazik Alturki Ghadah Aldehim Sapiah Sakri saiful farik mat yatin Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA(UiTM)Shah AlamMalaysia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Faculty of Computer and mathematical Sciences Universiti Teknologi MARA(UiTM)Shah AlamMalaysia

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.

关键词： Sentiment analysis sentiment lexicon machine learning imbalanced data deep learning method unemployment rate

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