Senti-COVID19: An Interactive Visual Analytics System for Detecting Public Sentiment and Insights Regarding COVID-19 From Social Media

Yu, Xuemin; Ferreira, Martha Dais; Paulovich, Fernando V.

doi:10.1109/access.2021.3111833

Cited by 14 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the paper, they tried to process tweets in real-time to monitor public opinions and trust regarding intervention measures that the government implemented. Other authors tried to examine the public's sentiment when announcements about the development of vaccines took place [93]. Yu et al [93] as well as Rahmanti et al [94] highlight that social media data can be used to monitor the public's reaction toward COVID-19 events such as restrictions and other government measures.…”

Section: ) Leadershipmentioning

confidence: 99%

“…Other authors tried to examine the public's sentiment when announcements about the development of vaccines took place [93]. Yu et al [93] as well as Rahmanti et al [94] highlight that social media data can be used to monitor the public's reaction toward COVID-19 events such as restrictions and other government measures. Goel and Sharma [95] specifically looked at highly influential people ("leaders") and used text analysis to cluster the tweets of this influential group.…”

Section: ) Leadershipmentioning

confidence: 99%

“…Polarity detection deals with classifying a text's sentiment into positive, negative, or neutral [23]. VADER can be used to explore the sentiment of a variety of topics [88], [93]. Lexicon-based approaches provide an uncomplicated way of studying novel topics since they do not require labeled data.…”

Section: ) Overview Of Classification Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

et al. 2023

View full text Add to dashboard Cite

On Twitter, COVID-19 is a highly discussed topic. People worldwide have used Twitter to express their viewpoints and feelings during the pandemic. Previous research has focused on particular topics such as the public's sentiment during the lockdown, their opinion on governmental measures, or their stance towards COVID-19 vaccines. However, until today, there is no comprehensive overview that presents possible areas of application for sentiment analysis of COVID-19 Twitter data. Therefore, this study reveals how sentiment analysis can provide relevant insights for managing the pandemic by applying a behavioral and social science lens. In this context, our systematic literature review focuses on machine learning-based sentiment analysis techniques and compares the best-performing classification algorithms for COVID-19related Twitter data. We performed a search in five databases, which are: IEEE Xplore DL, ScienceDirect, SpringerLink, ACM DL, and AIS Electronic Library. This search resulted in 40 papers published between October 2019 and January 2022 that used sentiment analysis to evaluate the public opinion on COVID-19-related topics, which we further investigated. Our research indicates that the best performing models in terms of accuracy are ensemble models that comprise various machine learning classifiers. Especially BERT and RoBERTa models provide the most promising results when fine-tuned on Twitter data. Our study aims to combine machine learning-based sentiment analysis and insights from social and behavioral science to provide decision-makers and public health experts with guidance on the application of sentiment analysis in the fight against the spread of COVID-19.

show abstract

Section: ) Leadershipmentioning

confidence: 99%

Section: ) Leadershipmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Substantial unstructured data are generated, which provides valuable knowledge and presents great opportunities for businesses, government agencies, and individuals to create new services. The exploitation of this unstructured data has created a new field, namely sentiment analysis, which has been widely applied in areas such as epidemic communication [ 40 ], international relations [ 41 ], and education policy [ 42 ].…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Public sentiment analysis on urban regeneration: A massive data study based on sentiment knowledge enhanced pre-training and latent Dirichlet allocation

Chen¹,

Ru-liang²

2023

PLoS ONE

View full text Add to dashboard Cite

Background Public satisfaction is the ultimate goal and an important determinant of China’s urban regeneration plan. This study is the first to use massive data to perform sentiment analysis of public comments on China’s urban regeneration. Methods Public comments from social media, online forums, and government affairs platforms are analyzed by a combination of Natural Language Processing, Knowledge Enhanced Pre-Training, Word Cloud, and Latent Dirichlet Allocation. Results (1) Public sentiment tendency toward China’s urban regeneration was generally positive but spatiotemporal divergences were observed; (2) Temporally, public sentiment was most negative in 2020, but most positive in 2021. It has remained consistently negative in 2022, particularly after February 2022; (3) Spatially, at the provincial level, Guangdong posted the most comments and Tibet, Shanghai, Guizhou, Chongqing, and Hong Kong are provinces with highly positive sentiment. At the national level, the east and south coastal, southwestern, and western China regions are more positive, as opposed to the northeast, central, and northwest regions; (4) Topics related to Shenzhen’s renovations, development of China’s urban regeneration and complaints from residents are validly categorized and become the public’s key focus. Accordingly, governments should address spatiotemporal disparities and concerns of local residents for future development of urban regeneration.

show abstract

“…With the pandemic as a backdrop, there have already remained two main kinds of COVID-19 data research in visualization: micro-perspective in scientific visualization with this virus that is analyzed from a biomedical perspective and combined with clinical medicine (Nguyen et al 2021;Liu et al 2021). And macro-perspective in multiple related COVID-19 datasets, which contain such infection cases, recovery, and mortality rates with COVID and connected with social factors like geographical (Goetschel et al 2021), social media and journalism (Yu et al 2021;Leite et al 2020), trajectory of human mobility (Yang et al 2022), and other factors (Antweiler et al 2021;Gharizadeh et al 2020;Hua et al 2020).…”

Section: Visual Analytics Of Covid-19 Datamentioning

confidence: 99%

User-centered visual explorer of in-process comparison in spatiotemporal space

Dong

Ian

Liu

et al. 2022

J Vis

View full text Add to dashboard Cite

We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback. Graphical abstract

show abstract

Senti-COVID19: An Interactive Visual Analytics System for Detecting Public Sentiment and Insights Regarding COVID-19 From Social Media

Cited by 14 publications

References 12 publications

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Public sentiment analysis on urban regeneration: A massive data study based on sentiment knowledge enhanced pre-training and latent Dirichlet allocation

User-centered visual explorer of in-process comparison in spatiotemporal space

Contact Info

Product

Resources

About