Exploring the Chinese Public’s Perception of Omicron Variants on Social Media: LDA-Based Topic Modeling and Sentiment Analysis

Wang, Han; Sun, Kun; Wang, Yuwei

doi:10.3390/ijerph19148377

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…Use this research method to study academic papers in the eld of health, focusing primarily on COVID-19 [ 24][25][26] [27] , attitudes toward lesbians and gay men [ 28] , psychology, such as depression[ 29] [30] , personality traits [ 31] , group cohesion [ 32] etc. So there is originality in using this research method to study the topic of "Industrial Workers' Health".…”

Section: Discussionmentioning

confidence: 99%

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

Wang,

Yao,

et al. 2024

Preprint

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Background Promoting the construction of a healthy China is a national strategy to comprehensively enhance the health quality of the Chinese nation and achieve coordinated development between people’s health and the economy and society. Strengthening safety production and occupational health work, improving working conditions, and enhancing the health quality of industrial workers are important aspects of building a healthy China. However, there is currently a lack of research on the content of speeches on online platforms such as Sina Weibo. Objective This study used a web crawler approach to obtain people’s opinions about “Industrial worker health”.We investigated the value of Chinese social media and related public perceptions and response. The goal was to determine if this data is suitable for public response.Through this exploration, the paper seeks to provide evidence-based recommendations to enhance the alignment of digital public opinion with the overarching goals of the “Healthy China” strategy, ultimately promoting the health and welfare of the industrial workforce. Methods This paper employed cutting-edge web scraping techniques to harvest data from the prominent social media platform, Sina Weibo. Through the application of sophisticated data collection tools, specifically the “Houyi Collector”, the study focuses on the keyword “industrial workers' health” to extract relevant information from Sina Weibo, thereby capturing the pulse of online discussions regarding the health concerns and awareness of industrial laborers.The gathered dataset is then meticulously analyzed using the Latent Dirichlet Allocation (LDA) topic modeling technique to identify and categorize the principal themes and trends that prevail in the online conversations. This analytic approach is intended to uncover the breadth and trajectory of public attention and attitudes toward the well-being of industrial workers within the context of the “Healthy China” initiative. Results By illuminating the digital footprint of health-related industrial worker discourses on a major Chinese social media platform, this research contributes a nuanced understanding of the virtual labor community’s engagement with health issues. Conclusions The insights derived from the LDA topic modeling are set to inform stakeholders, including policymakers, health advocates, and industrial enterprises, regarding the focal points and sentiments that shape the online narrative on industrial workers’ health in China.

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Section: Discussionmentioning

confidence: 99%

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

Wang,

Yao,

et al. 2024

Preprint

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“…The present study used the LDA for topic analysis of negative sentiment posts. The LDA is a popular topic-mining method (Xie et al, 2021 ; Ogbuokiri et al, 2022 ; Oliveira et al, 2022 ; Wang H. et al, 2022 ). However, it was difficult to determine the optimal number of topics.…”

Section: Discussionmentioning

confidence: 99%

“…Oliveira et al ( 2022 ) analyzed English Twitter feeds from 12 countries from January 2020 to April 2021, and found that the main negative emotions were fear and sadness. Wang H. et al ( 2022 ) studied public sentiment related to the Omicron variant on Sina Weibo and found that fear accounted for the largest proportion of negative emotions. These studies suggest that fear may be one of the more important negative emotions during an epidemic.…”

Section: Discussionmentioning

confidence: 99%

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China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo

et al. 2023

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The prevention and control of the coronavirus disease 2019 (COVID-19) epidemic in China has entered a phase of normalization. The basis for evaluating and improving public health strategies is understanding the emotions and concerns of the public. This study establishes a fine-grained emotion-classification model to annotate the emotions of 32,698 Sina Weibo posts related to COVID-19 prevention and control from July 2022 to August 2022. The Dalian University of Technology (DLUT) emotion-classification system was adjusted to form four pairs (eight categories) of bidirectional emotions: good-disgust, joy-sadness, anger-fear, and surprise-anticipation. A lexicon-based method was proposed to classify the emotions of Weibo posts. Based on the selected Weibo posts, the present study analyzed the Chinese public's sentiments and emotions. The results showed that positive sentiment accounted for 51%, negative sentiment accounted for 24%, and neutral sentiment accounted for 25%. Positive sentiments were dominated by good and joy emotions, and negative sentiments were dominated by fear and disgust emotions. The proportion of positive sentiments on official Weibo (accounts belonging to government departments and official media) is significantly higher than that on personal Weibo. Official Weibo users displayed a weak guiding effect on personal users in terms of positive sentiment and the two groups of users were almost completely synchronized in terms of negative sentiment. The linear discriminant analysis (LDA) was performed on the two negative emotions of fear and disgust in the personal posts. The present study found that the emotion of fear was mainly related to COVID-19 infection and death, control of people with positive nucleic acid tests, and the outbreak of local epidemic, while the emotion of disgust was mainly related to the long-term existence of the epidemic, the cost of nucleic acid tests, non-implementation of prevention and control measures, and the occurrence of foreign epidemics. These findings suggest that Chinese attitudes toward epidemic prevention and control are positive and optimistic; however, there is also a notable proportion of fear and disgust. It is expected that this study will help public health administrators to evaluate the effectiveness of possible countermeasures and work toward precise prevention and control of the COVID-19 epidemic.

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“…Their study performed data gathering from platform of Twitter for the time-frame from 3rd Dec. 2021 to 26th Dec. 2021. A research paper introduced by Wang et al ( 2022 ) aimed at exploring the public’s perception of Chines people against Omicron variant. They used data collected from Weibo for the time frame from 27 November 2021 to 30 March 2022, using LDA-based topic modeling and ontology of DLUT emotion.…”

Section: Related Workmentioning

confidence: 99%

Omicron virus emotions understanding system based on deep learning architecture

Khalid

Khalefa

Yassen

et al. 2023

J Ambient Intell Human Comput

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Emotions understanding has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron virus as a case study to implement a emotions analysis framework to explore the global attitude and sentiment toward Omicron variant as an expression of Positive feeling, Neutral, and Negative feeling. Because since December 2021. Omicron variant has gained obvious attention and wide discussions on social media platforms that revealed lots of fears and anxiety feeling, due to its rapid spreading and infection ability between humans that could exceed the Delta variant infection. Therefore, this paper proposes to develop a framework utilizes techniques of natural languages processing (NLP) in deep learning methods using neural network model of Bidirectional-Long-Short-Term-Memory (Bi-LSTM) and deep neural network (DNN) to achieve accurate results. This study utilizes textual data collected and pulled from the Twitter platform (users’ tweets) for the time interval from 11-Dec.-2021 to 18-Dec.-2021. Consequently, the overall achieved accuracy for the developed model is 0.946%. The produced results from carrying out the proposed framework for sentiment understanding have recorded Negative sentiment at 42.3%, Positive sentiment at 35.8%, and Neutral sentiment at 21.9% of overall extracted tweets. The acquired accuracy using data of validation for the deployed model is 0.946%.

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Exploring the Chinese Public’s Perception of Omicron Variants on Social Media: LDA-Based Topic Modeling and Sentiment Analysis

Cited by 9 publications

References 27 publications

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo

Omicron virus emotions understanding system based on deep learning architecture

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