Sentiment analysis for news and social media in COVID-19

Yu, Xinran; Zhong, Chao; Li, Dandan; Xu, Wei

doi:10.1145/3423333.3431794

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…Even though this was the first outbreak after nearly two months, the public was already well aware, and the news was more likely to report the number of cases with neutral-informing tone rather than in emotional sentiment. The same observation was observed in Xu et al, that public opinion was more affected in the beginning, and deeper into the pandemic, sentiment in online news was less polarized [ 44 ]. We also saw that negative tone decreased over time as positive tone increased at the end of the outbreak.…”

Section: Discussionsupporting

confidence: 77%

Using ‘infodemics’ to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July—September 2020

et al. 2022

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Background Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic. Methods We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve. We explored the associations between engagement, sentiment polarity, and other characteristics of online information with different outbreak phases using Poisson regression and multinomial logistic regression analysis. We assessed the frequency of keywords over time, and conducted a semantic analysis of keywords using word segmentation. Results We found a close association between collected online information and the evolution of the COVID-19 situation in Vietnam. Online information generated higher engagements during compared to before the outbreak. There was a close relationship between sentiment polarity and posts’ topics: the emotional tendencies about COVID-19 mortality were significantly more negative, and more neutral or positive about COVID-19 incidence. Online newspaper reported significantly more information in negative or positive sentiment than online forums or social media. Most topics of public concern followed closely the progression of the COVID-19 situation during the outbreak: development of the global pandemic and vaccination; the unfolding outbreak in Vietnam; and the subsiding of the outbreak after two months. Conclusion This study shows how online information can reflect a public health threat in real time, and provides important insights about public awareness and perception during different outbreak phases. Our findings can help public health decision makers in Vietnam and other low and middle income countries with high internet penetration rates to design more effective communication strategies during critical phases of an epidemic.

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Section: Discussionsupporting

confidence: 77%

Using ‘infodemics’ to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July—September 2020

et al. 2022

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“…It is challenging to define COVID-19's current role in society and, therefore, to predict its potential economic effect. Nevertheless, as a significant societal event, COVID-19 plays a crucial role in shaping public opinion (Maeda 2022;Malecki et al 2021;Yu et al 2020). By examining the relationship between the topics of "COVID-19" and "economy" in public opinion, we can hypothesize about COVID-19's possible economic effect.…”

Section: (6) If = Pearson(is Sm)mentioning

confidence: 99%

The game of lies by stock investors in social media: a study based on city lockdowns in China

Liu,

Son,

Lee

2024

Financ Innov

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The potential hypotheses for finance research based on social media sentiment revolve around the reliability of investor sentiment expressed on social media and the causal relationship between financial markets and this sentiment. The central hypothesis we focus on is derived from the "lie game" played by investors on social media. This study is the first to explore three states of this lie game in the context of the Chinese stock market: the "equilibrium state", the "confusion state", and the "subversion state". Our findings indicate that the "equilibrium" state is the typical state of the lie game, where increased investor sentiment results in more positive market behavior, and higher stock prices lead to increased investor sentiment. We also examine the effect of significant social events, such as the "lockdown in Wuhan" and the "lockdown in Shanghai", on the lie game's outcome. The successful lockdown in Wuhan and the public's opposition to the politicization of COVID-19 reinforced the "equilibrium" state of the game. However, the Shanghai lockdown's failure to promptly halt the spread of COVID-19 led to the intertwining of the economy and COVID-19 in public discourse, shifting the lie game's outcome from an "equilibrium state" to a "subversive state". We emphasize that the "confusion state" and "subversion state" outcomes of the lie game are concerning, and managing public opinion and the externalization of domestic conflicts can help reduce this risk. This study offers a fresh perspective on the traditional issues of investor sentiment reliability and the causal relationship between investor sentiment and stock markets.

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“…The authors in [20] used the keyword "COVID-19" to search tweets on Weibo, one of the largest Chinese social media platforms, and collected 45,987 tweets. They used a lexicon-based approach for sentiment analysis by matching their dataset with words in the sentiment dictionary.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

Said¹,

Tawfik²,

Makhlouf³

2023

IJACSA

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Now-a-days, social media platforms enable people to continuously express their opinions and thoughts about different topics. Monitoring and analyzing the sentiments of people is essential for governments and business organizations to better understand people's feelings and thoughts. The Coronavirus disease 2019 (COVID-19) has been one of the most trending topics on social media over the last two years. Consequently, one of the preventative measures to control and prevent the spread of the virus was vaccination. A dataset was formed by collecting tweets from Twitter for over a month from November 13th to December 31st, 2021. After data cleaning, the tweets were assigned a positive, negative, or neutral label using a natural language processing (NLP) sentiment analysis tool. This study aims to analyze people's public opinion towards the vaccination process against COVID-19. To fulfil this goal, an ensemble model based on deep learning (LSTM-2BiGRU) is proposed that combines long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU). The performance of the proposed model is compared to five traditional machine learning models, two deep learning models in addition to state-ofthe-art models. By comparing the results of the models used in this study, the results reveal that the proposed model outperforms all the machine and deep learning models employed in this work with a 92.46% accuracy score. This study also shows that the number of tweets that involve neutral, positive, and negative sentiments is 517496 (37%) tweets, 484258 (34%) tweets, and 409570 (29%) tweets, respectively. The findings indicate that the number of people carrying neutral sentiments towards COVID-19 immunization through vaccines is the highest among others.

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Sentiment analysis for news and social media in COVID-19

Cited by 15 publications

References 16 publications

Using ‘infodemics’ to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July—September 2020

Using ‘infodemics’ to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July—September 2020

The game of lies by stock investors in social media: a study based on city lockdowns in China

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

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