Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data

Kaur, Simranpreet; Kaul, Pallavi; Zadeh, Pooya Moradian

doi:10.1016/j.procs.2020.10.056

Cited by 47 publications

(35 citation statements)

References 14 publications

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“…Experiment results point out that most of the tweets were neutral while tweets in favor of the vaccine overtook the tweets against the vaccine. Kaur et al in their research paper [33] have collected 16,138 tweets from three different months of 2020 namely February, May, and June to monitor the polarity of tweets amid COVID-19. The number of negative tweets surpassed the neutral and positive tweets in all different time intervals as expected.…”

Section: Sentiment Polarity Assessment On Covid-19 Datamentioning

confidence: 99%

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

et al. 2021

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It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity trend and a shift since the start of the coronavirus to the vaccination drive across six countries. The findings suggest that people of neighboring countries have shown quite a similar attitude regarding the vaccination in contrast to their different reactions to the coronavirus outbreak.

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Section: Sentiment Polarity Assessment On Covid-19 Datamentioning

confidence: 99%

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

et al. 2021

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“…Then use the Naive Bayes algorithm because it has a high degree of accuracy in analyzing sentiment. In this study, it can be concluded that on average giving neutral comments with an accuracy of 86.43% obtained through the Rapid Miner tool [11].…”

Section: Literature Reviewmentioning

confidence: 72%

Analysis of twitter sentiment in COVID-19 era using fuzzy logic method

2021

joscex

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The sentiment is an assessment of attitudes towards certain events or things. Collecting opinion is known as a sentiment from existing data. This technique can also help analyze the opinions given by people in assessing certain objects. The best available source for gathering sentiment is the internet. In the era of the Covid-19 pandemic, many people access social media, especially Twitter to give their opinion on certain objects. Twitter is known as the social media that is accessed by users to post their opinions online. By using soft computing, especially fuzzy logic, it is possible to design, create and build bots that can analyze user opinions on Twitter. This model is used for data sentiment analysis on Twitter. The results showed that the sentiment analysis during the Covid-19 period was still dominant with positive tweets. As many as 48% of tweets are positive, 30% are negative tweets and 22% are neutral tweets. The use of applications to identify tweet sentiment during Covid-19 uses a combination of fuzzy logic methods with artificial intelligence. With the help of the Twitter API, you can get tweet data during the Covid-19 pandemic so you can find out the frequently used tweet sentiments.

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“…In current work, we present a well-organized machine learning model that has been employed into common COVID-19 oriented tweets where different regions are not specified like previous studies [18] , [28] , [48] , [49] . Both sentiment analysis and topics modelling were used to explore COVID-19 related themes than many works [18] , [20] , [22] , [24] , [27] , [48] , [49] , [50] , [51] . However, many machine learning classifiers have been implemented in which we compared our proposed model with more traditional analyses to evaluate performance.…”

Section: Discussionmentioning

confidence: 99%

“…Kaur et al. [24] translated 16,138 tweets into English and scrutinized sentiments and emotions using TextBlob and IBM Tone analyzer, respectively. Medford et al.…”

Section: Literature Reviewmentioning

confidence: 99%