TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

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dc.contributor.authorAslan, Serpil
dc.contributor.authorKiziloluk, Soner
dc.contributor.authorSert, Eser
dc.date.accessioned2026-06-19T06:41:15Z
dc.date.available2026-06-19T06:41:15Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractCOVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature.
dc.identifier.doi10.1007/s00521-023-08236-2
dc.identifier.endpage10328
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue14
dc.identifier.orcid0000-0002-8611-701X
dc.identifier.orcid0000-0001-8009-063X
dc.identifier.orcid0000-0002-0381-9631
dc.identifier.pmid36714074
dc.identifier.scopus2-s2.0-85146591275
dc.identifier.scopusqualityQ1
dc.identifier.startpage10311
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08236-2
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6137
dc.identifier.volume35
dc.identifier.wosWOS:000922720200002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectCovid-19
dc.subjectConvolutional Neural Network
dc.subjectArithmetic Optimization Algorithm
dc.subjectSentiment Analysis
dc.subjectTwitter
dc.titleTSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm
dc.typeArticle

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