Analysis of Public Agenda during Covid-19 Pandemics Based on Turkish and English Tweets Using Nonnegative Matrix Factorization and Hypothesis Testing

Abstract

In this study, Turkish and English tweets through Twitter Application Program Interface (API) between 1-31 January 2021 are analyzed with respect to Covid-19. The collected tweets are preprocessed, labeled with the Vader Sentiment library, and then analyzed by topic modeling with Nonnegative Matrix Factorization. The analysis show that the most frequently mentioned word is “vaccine/aşı” after “Covid”. The topics modelled in the study are grouped into themes and the themes are seen to be similar in both languages, which means that the Turkish and world agenda are not very different in terms of themes in pandemics. Moreover, hypothesis tests are conducted to understand whether language and time period are related to sentiment class. The results show that the Turkish people are more neutral about the Covid-19 issue than other people in the world during the given period of time. Moreover, independent of the language, there are more negative and neutral tweets in the first half of January 2021, whereas there are more positive tweets in the second half of the month. To the best of our knowledge, this is the first study to analyze Covid-19 related tweets in two languages to compare the local and global agendas using topic modeling, sentiment analysis, and hypothesis testing methods.