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. 2021;2(5):394.
doi: 10.1007/s42979-021-00789-0. Epub 2021 Jul 29.

Topics, Sentiments, and Emotions Triggered by COVID-19-Related Tweets from IRAN and Turkey Official News Agencies

Waseem Ahmad  1 , Bang Wang  1 , Han Xu  2 , Minghua Xu  2 , Zeng Zeng  3
Affiliations

Topics, Sentiments, and Emotions Triggered by COVID-19-Related Tweets from IRAN and Turkey Official News Agencies

Waseem Ahmad et al. SN Comput Sci. 2021.

Abstract

There is no doubt that the COVID-19 epidemic posed the most significant challenge to all governments globally since January 2020. People have to readapt after the epidemic to daily life with the absence of an effective vaccine for a long time. The epidemic has led to society division and uncertainty. With such issues, governments have to take efficient procedures to fight the epidemic. In this paper, we analyze and discuss two official news agencies' tweets of Iran and Turkey by using sentiment- and semantic analysis-based unsupervised learning approaches. The main topics, sentiments, and emotions that accompanied the agencies' tweets are identified and compared. The results are analyzed from the perspective of psychology, sociology, and communication.

Keywords: COVID-19; Emotion classification; Sentiment analysis; Topic modeling.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest. Some of the datasets used and the code generated during the current study are available from the corresponding author on reasonable request.

Figures

Fig. 1
Fig. 1
An overview of the research framework
Fig. 2
Fig. 2
Topic Modeling using LDA
Fig. 3
Fig. 3
Distribution of regular tweets, COVID-19 tweets, and new daily cases overtime. a Anadolu. b IRNA
Fig. 4
Fig. 4
Users’ interaction with regular tweets and COVID-19-related tweets. a Anadolu. b IRNA
Fig. 5
Fig. 5
Distribution of sentiments for COVID-19 tweets over the months. a Anadolu. b IRNA
Fig. 6
Fig. 6
Distribution of negative tweets and new daily deaths over time. a Anadolu. b IRNA
Fig. 7
Fig. 7
Distribution of emotions in different tweets
Fig. 8
Fig. 8
The correlation between emotions and months for Anadolu and IRNA. a Anadolu. b IRNA
Fig. 9
Fig. 9
The correlation between topics, months and user interactions for IRNA. a Months. b Users’ interaction
Fig. 10
Fig. 10
The correlation between topics, months and user interactions for Anadolu. a Months. b User interaction
Fig. 11
Fig. 11
The distribution of sentiments in topics for IRNA
Fig. 12
Fig. 12
The distribution of sentiments in topics for Anadolu
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