ABSTRACT
This study interprets the themes obtained as a result of the analysis of the internet news published during the Covid-19 pandemic in our country with Latent Dirichlet Allocation method. Apart from topic modeling, news documents were also subjected to category-based sentiment analysis and time-lapse graphics of published positive, negative and neutral news were shared. For this purpose, 37.724 news texts published in 5 different categories were collected. The period subject to analysis is December 2019 - February 2021. Although the effect of the virus has been alleviated at the moment, the themes that were on the agenda during the period when the effect of the virus was highest could be seen and the results were interpreted. © 2022 IEEE.
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. © 2022 Kauno Technologijos Universitetas. All rights reserved.
ABSTRACT
Education, like the large majority of domains, has been impacted by the rapid development of communications and technology. What was perceived before as an ideal, i.e., the enhancement of the learning process through modern techniques, now it has been rapidly transformed into a mandatory requirement due to the current COVID-19 pandemic [13]. Edutainment applications are meant to support the learning activities of young children even in the absence of an in-person teacher. In this paper, we present our proposal for developing smart edutainment applications for young children that allow automatic identification of the child and adaptation of the interaction flow based on the child’s emotions. © 2021, Springer Nature Switzerland AG.