| 1 | Machine Learning Approach for COVID-19 Detection on Twitter显示文摘Social networking services(SNSs)provide massive data that can be a very influential source of information during pandemic outbreaks.This 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 2020.Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions.This 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 messages.The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories,namely,confirmed deaths,recovered,and suspected.For 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 studies.The 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. | Samina Amin M.Irfan Uddin Heyam H.Al-Baity M.Ali Zeb M.Abrar Khan | 2021 | Computers, Materials & Continua2021，，8： | 1 |
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