Klasifikasi sentimen pada twitter terhadap WHO terkait COVID-19 menggunakan SVM, n-Gram, PSO

Hafidz, Noor; Yanti Liliana, Dewi; id,

On March 2020 World Health Organization (WHO) has declared COVID-19 as global pandemic. As special agency of United Nation who responsible for international public healthy, WHO has done various actions to reduce this pandemic spreading rate. However, the handling of COVID-19 by WHO is not free from a number of controversies that gave rise to criticism and public opinion on the Twitter platform. In this research, a machine learning based classifier model has been made to determine the opinion or sentiment of the tweet. The dataset used is a set of tweets containing the phrase WHO and COVID-19 in period of March 1st until May 6th, 2020 consisting of 4000 tweets with positive sentiments and 4000 tweets with negative sentiments. The proposed classifier model combined Support Vector Machine (SVM), N-Gram and Particle Swarm Optimization (PSO). The classifier model performance is evaluated using the value of Accuracy, Precision, Recall, and Area Under ROC Curve (AUC). Based on experiments conducted, the combination of SVM, N-gram (bigram), and PSO produced a pretty good performance in classifying tweet sentiment with values of Accuracy 0,755, Precision 0,719, Recall 0,837, and AUC 0,844.