Analysis of sentiments conveyed through Twitter concerning COVID-19

Due to the social and economic fallout from COVID-19 pandemic, we sought gauge attitudes of network users, in this case, Twitter, towards topic using a sentiment analysis approach. We collected 178,683 tweets Twitter API based on queries for high-frequency hashtag #covid19. After preprocessing step, classified them binary way (positive negative) according their intensity (valence) VADER model then NRCLex dictionary, which allows us classify feelings affective class. The results suggest that overall, detected through are positive. In addition, users seem be interestedin pandemic as trend rather than related other or aspects.