Weibo users perception of the COVID-19 pandemic on Chinese social networking service (Weibo): sentiment analysis and fuzzy-c-means model

Feng Han, Ying-Dan Cao, Ziheng Zhang, Hongjian Zhang, Tomohiko Aoki, Katsuhiko Ogasawara
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引用次数: 2

Abstract

Background: Over the last decade, social media analysis tools have been used to monitor public sentiment and communication methods for public health emergencies such as the Ebola and Zika epidemics. Research articles have indicated that many outbreaks and pandemics could have been promptly controlled if experts considered social media data. With the World Health Organization (WHO) pandemic statement and various governments government action on the disease, various sentiments regarding coronavirus disease 2019 (COVID-19) have spread across the world. Therefore, sentiment analyses in studying pandemics, such as COVID-19, are important based on recent events. Methods: The Term Frequency-Inverse Document Frequency (TF-IDF) method was used to extract keywords from the 850,083 content of Weibo from January 24, 2020, to March 31, 2020. Then the Latent Dirichlet Allocation (LDA) was used to perform topic analysis on the keywords. Finally, the fuzzy-c-means method was used to divide the content of Weibo into seven categories of emotions: fear, happiness, disgust, surprise, sadness, anger, and good. And the changes in emotion were tracked over time. Results: The results indicated that people showed “surprise” overall (55.89%);however, with time, the “surprise” decreased. As the knowledge regarding the COVID-19 increased, the “surprise” of the citizens decreased (from 59.95% to 46.58%). Citizens’ feelings of “fear” and “good” increased as the number of deaths associated with COVID-19 increased (“fear”: from 15.42% to 20.95% “good”: 10.31% to 18.89%). As the number of infections was suppressed, the feelings of “fear” and “good” diminished (“fear”: from 20.95% to 15.79% “good”: from 18.89% to 8.46%). Conclusions: The findings of this study indicate that people’s feelings were analyzed regarding the COVID-19 pandemic in three stages over time. In the beginning, people’s emotions were primarily “surprised”;however after the outbreak, people’s “surprise” decreased with increasing knowledge. At the end of the phase, I of the COVID-19 pandemic, people’s “fear” and “good” feelings were diminished as the epidemic was suppressed. People’s interest shifted from China to other countries and their concern about the situation in other countries. © Journal of Medical Artificial Intelligence. All rights reserved.
微博用户对新冠肺炎疫情的认知:情绪分析和模糊c-均值模型
背景:在过去十年中,社交媒体分析工具被用于监测公众情绪和应对埃博拉和寨卡疫情等突发公共卫生事件的沟通方法。研究文章表明,如果专家考虑社交媒体数据,许多疫情和流行病本可以得到迅速控制。随着世界卫生组织(世界卫生组织)的疫情声明和各国政府对该疾病的行动,关于2019冠状病毒病(新冠肺炎)的各种情绪在世界各地蔓延。因此,基于最近的事件,在研究新冠肺炎等流行病时进行情绪分析很重要。方法:采用词频逆文档频率(TF-IDF)方法,从2020年1月24日至2020年3月31日的850083条微博内容中提取关键词。然后使用潜在狄利克雷分配(LDA)对关键词进行主题分析。最后,使用模糊c-均值方法将微博内容分为七类情绪:恐惧、快乐、厌恶、惊讶、悲伤、愤怒和善良。情绪的变化随着时间的推移而被追踪。结果:调查结果显示,人们总体上表现出“惊讶”(55.89%);然而,随着时间的推移,“惊喜”减少了。随着对新冠肺炎的了解增加,市民的“惊喜”减少(从59.95%降至46.58%)。随着与新冠肺炎相关的死亡人数增加,市民对“恐惧”和“美好”的感觉增加(“恐惧”:从15.42%增至20.95%“美好”:10.31%至18.89%),“恐惧”和“好”的感觉减少(“恐惧”:从20.95%到15.79%“好”:从18.89%到8.46%)。一开始,人们的情绪主要是“惊讶”;然而,疫情爆发后,人们的“惊喜”随着知识的增加而减少。在新冠肺炎大流行的第一阶段结束时,人们的“恐惧”和“美好”情绪随着疫情的抑制而减弱。人们的兴趣从中国转移到其他国家,并对其他国家的局势表示担忧。©《医学人工智能杂志》。保留所有权利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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