Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning SchemeArticle Published on 2021-11-152022-10-29 Journal: Sensors (Basel, Switzerland) [Category] COVID-19, MERS, SARS, [키워드] Algorithm autoencoder collected competing COVID-19 data prediction dataset deep learning ensemble machine learning Evolution feature filtered involved Involving learning media message N-gram N-gram feature extraction network outperform performed platform predict random Research selected social media Support SVM training data Twitter Data [DOI] 10.3390/s21227582 PMC 바로가기 [Article Type] Article
EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweetsResearch article Published on 2021-05-012022-10-05 Journal: Online Social Networks and Media [Category] 치료기술, [키워드] Accuracy activity affecting Analysis automatically changed coronavirus COVID-19 COVID-19 data data analytics dataset detect develop Emotion analysis emotional Health lockdown machine machine learning mental health negative outperform pandemic physically response responsible selected Topics tracker Twitter Data USA [DOI] 10.1016/j.osnem.2021.100135 [Article Type] Research article
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning ApproachOriginal Paper Published on 2020-11-252022-10-30 Journal: Journal of Medical Internet Research [Category] COVID-19, SARS, [키워드] allocation analyzed approach collected confirmed case COVID-19 COVID-19 case COVID-19 pandemic death death rates deaths dominant emotion Government Health authority identify infodemic infodemiology infoveillance Latent machine learning machine learning approach measure mental health concern objective occur pandemic preventive measures psychological reaction public discussion public health emergency public health measure public sentiment Research response Result second wave social media spread of COVID-19 The United States Twitter Twitter Data virus with COVID-19 [DOI] 10.2196/20550 PMC 바로가기 [Article Type] Original Paper