Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows social media analysis used as crisis detector (e.g., understanding the sentiment users regarding various outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known coronavirus, has affected everyone worldwide in 2020. Streaming Twitter have revealed status COVID-19 outbreak most regions. focuses on identifying patients using tweets without requiring medical records to find messages (tweets). For this purpose, we propose herein an intelligent model traditional machine learning-based approaches, such support vector (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with help term frequency inverse document (TF-IDF) detect messages. proposed classifies into four categories, namely, confirmed deaths, recovered, suspected. experimental analysis, tweet are analyzed evaluate results learning approaches. A benchmark dataset for is developed future research studies. experiments show approach promising detecting overall accuracy, precision, recall, F1 score between 70% 80% confusion matrix approaches (i.e., SVM, NB, LR, RF, DT) TF-IDF feature extraction technique.