菲律宾COVID-19疫苗相关Facebook评论的主题建模、分支辅助情感分析和疫苗品牌声誉分析
Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the Philippines
摘要：
菲律宾的疫苗犹豫和其他与COVID-19相关的担忧和投诉在社交媒体上很明显。识别这些不同的话题和情绪非常重要，以便衡量公众意见，利用这些见解制定政策，并做出必要的调整或行动，以改善管理机构和COVID-19疫苗本身的公众形象和声誉。本文提出了一种半监督机器学习管道来执行主题建模、情感分析和疫苗品牌声誉分析，以深入了解菲律宾人在Facebook上的全国民意。该方法利用多语言版本的双向编码器表示从Transformers或BERT的主题建模，层次聚类，五个不同的分类器的情感分析，余弦相似度的BERT主题嵌入疫苗品牌声誉分析。研究结果表明，任何类型的COVID-19错误信息都是COVID-19舆论的一个紧急属性，而COVID-19错误信息的检测可能是一项无人监督的任务。情感分析结合层次聚类分析发现，在主题模型外推的25个主题中，有21个是负面主题。每当菲律宾卫生部发布有关其他国家COVID-19疫情的信息时，此类负面评论就会激增。此外，同一机构在Facebook帖子上的大量笑声反应-没有任何幽默内容-表明这些帖子的反应者往往会做出反应，不是因为帖子是关于什么的，而是因为谁发布了它们。
英文摘要：
Vaccine hesitancy and other COVID-19-related concerns and complaints in the Philippines are evident on social media. It is important to identify these different topics and sentiments in order to gauge public opinion, use the insights to develop policies, and make necessary adjustments or actions to improve public image and reputation of the administering agency and the COVID-19 vaccines themselves. This paper proposes a semi-supervised machine learning pipeline to perform topic modeling, sentiment analysis, and an analysis of vaccine brand reputation to obtain an in-depth understanding of national public opinion of Filipinos on Facebook. The methodology makes use of a multilingual version of Bidirectional Encoder Representations from Transformers or BERT for topic modeling, hierarchical clustering, five different classifiers for sentiment analysis, and cosine similarity of BERT topic embeddings for vaccine brand reputation analysis. Results suggest that any type of COVID-19 misinformation is an emergent property of COVID-19 public opinion, and that the detection of COVID-19 misinformation can be an unsupervised task. Sentiment analysis aided by hierarchical clustering reveal that 21 of the 25 topics extrapolated by topic modeling are negative topics. Such negative comments spike in count whenever the Department of Health in the Philippines posts about the COVID-19 situation in other countries. Additionally, the high numbers of laugh reactions on the Facebook posts by the same agency -- without any humorous content -- suggest that the reactors of these posts tend to react the way they do, not because of what the posts are about but because of who posted them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
