期刊文章JMIR出版物揭示塞尔维亚COVID-19疫苗犹豫背后的原因:卡塔尔世界杯8强波胆分析基于情感的主题建模%A ljajiki,Adela %A prodanoviki,Nikola %A Medvecki,Darija %A Bašaragin,Bojana %A mitroviki,Jelena %+塞尔维亚人工智能研究与发展研究所Fruškogorska 1，诺维萨，21000,381 652626347。adela.ljajic@ivi.ac.rs %K主题建模%K情绪分析%K LDA %K NMF %K BERT %K疫苗犹豫%K COVID-19 %K Twitter %K塞尔维亚语处理%K疫苗%K公共卫生%K NLP %K疫苗接种%K塞尔维亚%D 2022 %7 17.11.2022 %9原文%J J Med Internet Res %G English %X背景:自第一个COVID-19疫苗出现以来，自动确定公众对其态度的趋势越来越大。尤其重要的是要找到疫苗犹豫的原因，因为这与大流行的延长直接相关。自然语言处理(NLP)和公共卫生研究人员已经转向社交媒体(如Twitter、Reddit和Facebook)，以获取用户创建的内容，他们可以从中评估公众对疫苗接种的看法。为了自动处理这些内容，他们使用了许多NLP技术，其中最著名的是主题建模。主题建模支持对文本中的隐藏主题进行自动发现和分组。当应用于表达对疫苗接种的负面情绪的内容时，它可以直接洞察疫苗犹豫的原因。目的:本研究应用自然语言处理方法对塞尔维亚语负面推文中疫苗接种相关推文进行情感极性分类，揭示疫苗犹豫的原因。方法:收集2批推特中涉及COVID-19疫苗接种某些方面的信息，研究疫苗犹豫背后的态度和信念。 The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. Results: The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. Conclusions: This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process. %M 36301673 %R 10.2196/42261 %U //www.mybigtv.com/2022/11/e42261 %U https://doi.org/10.2196/42261 %U http://www.ncbi.nlm.nih.gov/pubmed/36301673