TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment in COVID-19 Tweets

Abstract

COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance of traditional and TClustVID classification methods. TClustVID showed higher performance compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation.

Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities

“…These include pulmonary diseases, cardiovascular diseases, kidney disease, type 2 diabetes and hypertension [ 47 , 56 ]. Sequential Organ Failure Assessment (SOFA) scores have been reported significantly greater in SARS-CoV-2 associated deaths [ 49 , 69 ]. While COVID- \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$19$\end{document} primarily affects the respiratory system in the early stages of the disease, while it also affects the cardiovascular system of patients, greatly increasing their risk of fatality [ 22 , 28 , 68 ].…”

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

“…Waiker (2020) used deep learning methods to review and critically appraise published and preprint reports of prediction models for COVID-19 patients. In particular, several study works ( Afshar et al, 2020 ; Asnaoui et al, 2020 ; Corman et al, 2020 ; Fomsgaard and Rosenstierne, 2020 ; Forbes, 2020 ; Ghoshal and Tucker, 2020 ; Gozes et al, 2020 ; Hall et al, 2020 ; Healthitanalytics, 2020 ; Hu et al, 2020a ; Hu et al, 2020b ; IBM, 2020 ; Loey et al, 2020 ; Maghdid et al, 2020 ; Narin et al, 2020 ; Pal et al, 2020 ; Pham et al, 2020 ; Qi et al, 2020 ; Rao and Vazquez, 2020 ; Satu et al, 2020 ; Sodhro et al, 2019 ; Yan et al, 2020 ; Zhang et al, 2020 ; Zheng et al, 2020 ) have used machine learning techniques, including big data techniques, to process COVID-19 data to determine the spread of disease, predict the risk of disease, and to assess the diagnosis of disease, number of incidences, and healthcare facilities.…”

Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

“…Machine Learning can be utilized to extract useful information from extensive datasets and build intelligent prediction models for healthcare, as well as other features of this viral pandemic [12][13][14][15][16][17]. Along with PCR-based and antibody based virus test predictions, COVID-19 cases can be identified by chest X-ray [18,19] and computerized tomography (CT) [20,21] images using machine learning [22,23].…”