Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach

Ghasiya, Piyush; Okamura, Koji

doi:10.1109/access.2021.3062875

Cited by 98 publications

(73 citation statements)

References 26 publications

(29 reference statements)

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“…As for South Korea, with 10,076 articles and 127 topics, the country's top five topics were economic relief, geopolitics, impact on retail, lower GDP and COVID-19 cases. To conclude, topic modeling revealed common themes to be education, economy, US and sports across four nations and the sentiment analysis indicated that the UK had 73.23% of news being negative, in contrast to South Korea's 54.47% being positive [19]. Fourth, based on media framework theory, the study of Thirumaran et al [23] focused on newspapers from Singapore and New Zealand and investigated the relationship between destination crisis management strategy and the effects of news portrayal out of traveling concerns.…”

Section: Sentiment Analysis and Text Mining With Social And News Mediamentioning

confidence: 97%

“…Most COVID-19-related sentiment analysis and text mining studies have analyzed social media data, while few comparative studies have suggested that the topic coverage of social media is narrower, i.e., the sentiment is more likely to be negative and has a shorter life span than that of news media [17,18]. By contrast, news articles that are written by journalists and subject matter experts present facts that make them more objective [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Investigating COVID-19 News before and after the Soft Lockdown: An Example from Taiwan

2021

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COVID-19 caused an unprecedented public health crisis and was declared a global pandemic on 11 March 2020, by the World Health Organization. The Taiwanese government’s early deployment mitigated the effect of the pandemic, yet the breakout in May 2021 brought a new challenge. This study focuses on examining Taiwanese newspaper articles regarding the government response before and after the soft lockdown, collecting 125,570 articles reported by three major news channels from 31 December 2019, to 30 June 2021, and splitting them into four stages. Latent Dirichlet Allocation topic modeling and sentiment analysis were used to depict the overall picture of Taiwan’s pandemic. While the news media focused on the impact and shock of the pandemic in the initial stage, prevention measures were more present in the last stage. Then, to focus on the government response indicators, we retrieved 31,089 related news from 125,570 news articles and categorized them into ten indicators, finding the news centered on the fundamental measures that were taken early and that were transformed into advanced measures in the latest and hardest period of the pandemic. Furthermore, this paper examines the temporal distribution of the news related to each indicator with the support of a sentiment analysis of the news’ titles and content, indicating the preparation of Taiwanese society to confront the pandemic.

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Section: Sentiment Analysis and Text Mining With Social And News Mediamentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Investigating COVID-19 News before and after the Soft Lockdown: An Example from Taiwan

2021

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“…Some studies combined both topic modeling and sentiment analysis to assess people's sentiments on multiple aspects of the pandemic. In [17], the authors investigated COVID-19 related news across four countries using Top2vec for topic modeling and RoBERTa for sentiment analysis. In [18], the authors used multiple topic modeling techniques and sentiment analysis algorithms to analyze content from Brazil and USA based twitter accounts.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management

Ghanem

Asaad

Hafidi

et al. 2021

IJERPH

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The impact of COVID-19 on socio-economic fronts, public health related aspects and human interactions is undeniable. Amidst the social distancing protocols and the stay-at-home regulations imposed in several countries, citizens took to social media to cope with the emotional turmoil of the pandemic and respond to government issued regulations. In order to uncover the collective emotional response of Moroccan citizens to this pandemic and its effects, we use topic modeling to identify the most dominant COVID-19 related topics of interest amongst Moroccan social media users and sentiment/emotion analysis to gain insights into their reactions to various impactful events. The collected data consists of COVID-19 related comments posted on Twitter, Facebook and Youtube and on the websites of two popular online news outlets in Morocco (Hespress and Hibapress) throughout the year 2020. The comments are expressed in Moroccan Dialect (MD) or Modern Standard Arabic (MSA). To perform topic modeling and sentiment classification, we built a first Universal Language Model for the Moroccan Dialect (MD-ULM) using available corpora, which we have fine-tuned using our COVID-19 dataset. We show that our method significantly outperforms classical machine learning classification methods in Topic Modeling, Emotion Recognition and Polar Sentiment Analysis. To provide real-time infoveillance of these sentiments, we developed an online platform to automate the execution of the different processes, and in particular regular data collection. This platform is meant to be a decision-making assistance tool for COVID-19 mitigation and management in Morocco.

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“…Also, a study done by Saif Hassan, Fernández Miriam, He Yulan and Alani Harith [15] provided a comprehensive overview of "On Stop-words, Filtering and Data Sparsity for Sentiment Analysis of Twitter" in 2014. Many such works are done by the researchers in [16][17][18][19][20][21][22][23][24][25][26][27]. Above research works are based on sentiment analysis using different methods on social media data in different domains.…”

Section: Related Workmentioning

confidence: 99%

“…The tweets are classi ed into 3 polarity; positive, negative, neutral sentiment. Our model consists of results from 3 types of Machine Learning models [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]; Decision Tree, SVM, and Logistic Regression. The simulation is performed better under this environment with 88.67% accuracy in SVM.…”

Section: Trainingmentioning

confidence: 99%

A Social Media Sentiment Analysis: Machine Intelligence Model For Worldwide Covid-19 Vaccination Using Twitter Data

Jena

Bhoi

Behera

et al. 2021

Preprint

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Understanding human emotions is one of the crucial aspects when we are to take action. Our emotions dictate our apparent behaviors. In simple words, what we feel inside can predict things about what we would do. This creates a huge opportunity for government and businesses industry to understand and predict people's behaviors. There has been some really great research done on this with high accuracy. Recently, Covid-19 vaccination process is a challenging task going on all over the world and it is necessary to explore people’s reaction over this for more effective vaccination process spread. In this paper, wetried to understand an event (Covid-19 vaccination) with a relatively simple model with decent accuracy compared to other sophisticated models. We use simple machine learning models to train and deploy it over the network. We have used KNIME Analytical Platform to design and implement our model as it provides end-to-end analytics. We have managed to get 88.67% accuracy and Cohen’s kappa 0.789 with SVM model by tuning some parameters. The model is deployed on Twitter data. This paper shows our efforts trying to make a simple model to analyze an event (Covid-19 vaccination) and understand people's emotions towards the event. The methodology involves identifying important topics (terms) and finding out the sentiment (positive, negative, neutral). This paper tries to find a low-cost solution to analyze an event and provide data-driven insights from it without involving sophisticated algorithms.

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Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach

Cited by 98 publications

References 26 publications

Investigating COVID-19 News before and after the Soft Lockdown: An Example from Taiwan

Investigating COVID-19 News before and after the Soft Lockdown: An Example from Taiwan

Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management

A Social Media Sentiment Analysis: Machine Intelligence Model For Worldwide Covid-19 Vaccination Using Twitter Data

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