Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach

Bhagat, Kaushal Kumar; Mishra, Sanjaya; Dixit, Alakh; Chang, Chun Yen

doi:10.3390/su13063346

Cited by 46 publications

(32 citation statements)

References 38 publications

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“…[ 14 ] used TextBlob to perform sentiment analysis towards COVID-19 vaccination by web scraping 154 articles on blogs and online newspapers during the COVID-19 pandemic. They concluded that over 90% of the articles contained positive sentiments towards vaccinations [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Twitter sentiment analysis from Iran about COVID 19 vaccine

Nezhad

Deihimi

2022

Diabetes & Metabolic Syndrome: Clinical Research & Revi

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Background and aims The development of vaccines against COVID-19 has been a global purpose since the World Health Organization declared the pandemic. People usually use social media, especially Twitter, to transfer knowledge and beliefs on global concerns like COVID-19-vaccination, hence, Twitter is a good source for investigating public opinions. The present study aimed to assess Persian tweets to (1) analyze Iranian people's view toward COVID-19 vaccination. (2) Compare Iranian views toward a homegrown and imported COVID-19-vaccines. Methods First, a total of 803278 Persian tweets were retrieved from Twitter, mentioning COVIran Barekat (the homegrown vaccine), Pfizer/BioNTech, AstraZeneca/Oxford, Moderna, and Sinopharm (imported vaccines) between April 1, 2021 and September 30, 2021. Then, we identified sentiments of retrieved tweets using a deep learning sentiment analysis model based on CNN-LSTM architecture. Finally, we investigated Iranian views toward COVID-19-vaccination. Results (1) We found a subtle difference in the number of positive sentiments toward the homegrown and foreign vaccines, and the latter had the dominant positive polarity. (2) The negative sentiment regarding homegrown and imported vaccines seems to be increasing in some months. (3) We also observed no significant differences between the percentage of overall positive and negative opinions toward vaccination amongst Iranian people. Conclusions It is worrisome that the negative sentiment toward homegrown and imported vaccines increases in Iran in some months. Since public healthcare agencies aim to increase the uptake of COVID-19 vaccines to end the pandemic, they can focus on social media such as Twitter to promote positive messaging and decrease opposing views.

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Section: Related Workmentioning

confidence: 99%

Twitter sentiment analysis from Iran about COVID 19 vaccine

Nezhad

Deihimi

2022

Diabetes & Metabolic Syndrome: Clinical Research & Revi

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“…There have been several works related to analyzing the Twitter dataset on different topics during the COVID-19 pandemic [12][13][14][15]. Only a few studies focus on the Twitter data related to COVID-19 vaccination [16,17].…”

Section: Related Workmentioning

confidence: 99%

“…They aim at identifying the public discussion on addiction on Twitter during the COVID pandemic but have not focused on sentiment analysis on addiction due to the pandemic. In [13], the authors worked with Twitter data related to "Mask". They found that the volume and polarity of mask related tweets has greatly increased during the timeline from 17 March 2020 to 27 July 2020.…”

Section: Related Workmentioning

confidence: 99%

“…They compared 13 different text preprocessing strategies for correlating the sentiment scores of the tweets with bitcoin price. Bhagat et al [15] performed sentiment analysis using TextBlob on online education by webscraping 154 articles from online news and blogging websites during the COVID-19 pandemic. Their results show that over 90% of the articles are positive, and, in general, the blogs have been more positive than the newspaper articles.…”

Section: Related Workmentioning

confidence: 99%

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COVID-19 Vaccination Awareness and Aftermath: Public Sentiment Analysis on Twitter Data and Vaccinated Population Prediction in the USA

Sattar

Arifuzzaman

2021

Applied Sciences

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Social media, such as Twitter, is a source of exchanging information and opinion on global issues such as COVID-19 pandemic. In this study, we work with a database of around 1.2 million tweets collected across five weeks of April–May 2021 to draw conclusions about public sentiments towards the vaccination outlook when vaccinations become widely available to the population during the COVID-19 pandemic. We deploy natural language processing and sentiment analysis techniques to reveal insights about COVID-19 vaccination awareness among the public. Our results show that people have positive sentiments towards taking COVID-19 vaccines instead of some adverse effects of some of the vaccines. We also analyze people’s attitude towards the safety measures of COVID-19 after receiving the vaccines. Again, the positive sentiment is higher than that of negative in terms of maintaining safety measures against COVID-19 among the vaccinated population. We also project that around 62.44% and 48% of the US population will get at least one dose of vaccine and be fully vaccinated, respectively, by the end of July 2021 according to our forecast model. This study will help to understand public reaction and aid the policymakers to project the vaccination campaign as well as health and safety measures in the ongoing global health crisis.

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“…While the majority of studies covered sentiment analysis of COVID-19 tweets in general, some studies put focus on more specific topics, such as vaccination [33,34] or online education [35,36]. There is also a small number of studies that focused more on the comparison of different algorithms for sentiment classification of COVID-19 tweets than on their application and analysis of results.…”

Section: Nlp-based Analyses Of Covid-19 Related Tweetsmentioning

confidence: 99%

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

et al. 2021

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This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian language. We collected and analysed 206,196 tweets related to COVID-19 and constructed a dataset of 10,000 tweets which we manually annotated with a sentiment label. We trained the Cro-CoV-cseBERT language model for the representation and clustering of tweets. Additionally, we compared the performance of four machine learning algorithms on the task of sentiment classification. After identifying the best performing setup of NLP methods, we applied the proposed framework in the task of characterisation of COVID-19 tweets in Croatia. More precisely, we performed sentiment analysis and tracked the sentiment over time. Furthermore, we detected how tweets are grouped into clusters with similar themes across three pandemic waves. Additionally, we characterised the tweets by analysing the distribution of sentiment polarity (in each thematic cluster and over time) and the number of retweets (in each thematic cluster and sentiment class). These results could be useful for additional research and interpretation in the domains of sociology, psychology or other sciences, as well as for the authorities, who could use them to address crisis communication problems.

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Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach

Cited by 46 publications

References 38 publications

Twitter sentiment analysis from Iran about COVID 19 vaccine

Twitter sentiment analysis from Iran about COVID 19 vaccine

COVID-19 Vaccination Awareness and Aftermath: Public Sentiment Analysis on Twitter Data and Vaccinated Population Prediction in the USA

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

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