Machine Learning Approach for COVID-19 Detection on Twitter

作     者：Samina Amin M.Irfan Uddin Heyam H.Al-Baity M.Ali Zeb M.Abrar Khan 

作者机构：Institute of ComputingKohat University of Science and TechnologyKohat26000Pakistan Department of Information TechnologyCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia 

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

年 卷 期：2021年第68卷第8期

页      面：2231-2247页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：supported by a grant from the Research Center of the Female Scientific and Medical Colleges Deanship of Scientific Research King Saud University 

主　　题：Artificial intelligence coronavirus COVID-19 pandemic social network Twitter machine learning natural language processing 

摘      要：Social networking services(SNSs)provide massive data that can be a very influential source of information during pandemic *** study shows that social media analysis can be used as a crisis detector(e.g.,understanding the sentiment of social media users regarding various pandemic outbreaks).The novel Coronavirus Disease-19(COVID-19),commonly known as coronavirus,has affected everyone worldwide in *** Twitter data have revealed the status of the COVID-19 outbreak in the most affected *** study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages(tweets).For this purpose,we propose herein an intelligent model using traditional machine learning-based approaches,such as support vector machine(SVM),logistic regression(LR),naïve Bayes(NB),random forest(RF),and decision tree(DT)with the help of the term frequency inverse document frequency(TF-IDF)to detect the COVID-19 pandemic in Twitter *** proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories,namely,confirmed deaths,recovered,and *** the experimental analysis,the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches.A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research *** experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy,precision,recall,and F1 score between 70%and 80%and the confusion matrix for machine learning approaches(i.e.,SVM,NB,LR,RF,and DT)with the TF-IDF feature extraction technique.