Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2024-02-06 DOI:10.2196/37881
Ryuichiro Ueda, Feng Han, Hongjian Zhang, Tomohiro Aoki, Katsuhiko Ogasawara
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引用次数: 0

Abstract

Background: The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan's early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions.

Objective: This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period.

Methods: We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords "corona," "COVID-19," and "new pneumonia" from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups.

Results: The analysis revealed a mix of positive and negative sentiments, with "joy" significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as "worry" and "disgust," were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group.

Conclusions: This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis.

日本 Twitter 用户的情感分析:COVID-19 感染传播初期的验证。
背景:COVID-19 在我省的爆发引发了全球性的行为限制,影响了公众的心理健康。情感分析是一种从文本数据中评估个人和公众情绪的工具,在疫情中变得越来越重要。本研究重点关注日本在 COVID-19 期间的早期公共卫生干预措施,利用信息发病学中的情感分析来评估公众在社交媒体上对这些干预措施的情感:本研究旨在调查日本首次宣布进入紧急状态前后公众情绪和情感的变化。通过分析用户生成的推文和转发,本研究旨在发现这一关键时期的情绪反应模式:我们使用 4,894,009 条包含关键词 "日冕"、"COVID-19 "和 "新肺炎 "的推文,对推特数据进行了逐日分析,分析时间为 2020 年 3 月 23 日至 4 月 21 日,即日本首次宣布进入紧急状态前后约两周。我们还将推文数据处理成每个单词的向量,对情感词典中的单词采用聚类分析的一种--模糊-C-Means(FCM)方法,建立了七个情感聚类(负面:愤怒、悲伤、惊讶、厌恶;中性:焦虑;正面:信任和喜悦),并按推文组和转发组进行情感分析:分析结果显示,在宣布进入紧急状态后,转发组中的 "喜悦 "情绪明显增加。消极情绪,如 "担忧 "和 "厌恶",在推文组和转发组中都很普遍。此外,与推特组相比,转发组倾向于分享更多负面内容:本研究对日本的推文和转发(RTs)进行了情感分析,以探讨在日本 COVID-19 的早期阶段,即首次宣布进入紧急状态前后两周内的公众情感。分析结果显示了积极(喜悦)和消极(焦虑、厌恶)的混合情绪。值得注意的是,在宣布紧急状态后,RT 组的喜悦感有所增加,但与 Tweet 组相比,该组也表现出分享更多负面内容的倾向。研究表明,由于对预防感染措施的期望,紧急状态增强了人们的积极情绪,但负面信息也获得了关注。研究结果提出了通过网络分析进一步探索的可能性:
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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