Abstract
Today, sentiment analysis is in high use to understand user reactions. In this paper, the authors have discussed this topic using news and Twitter texts as sources of data. They use TextBlob, VADER, and IBM Watson NLU as sentiment analysis tools. The news sentiment analysis data is from January to July 2020, classified under each tool. The authors get almost the same result from all of them. February shows having the maximum negative polarity news, followed by June. While Twitter data of each month when classified under each sentiment analysis tool shows the same kind of result for all the months, March has the maximum negative polarity and maximum positive polarity is seen in January. The aim of this paper is to show that sentiment analysis on newspaper content can help common people to know the bias in newspapers to prevent more negative impact on readers especially during a pandemic like COVID-19. The comparison drawn between the news data sentiment analysis and the same with Twitter data has a good correlation but still shows a difference in sentiment.
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