Building models to detect vaccine attitudes on social media is challenging
because of the composite, often intricate aspects involved, and the limited
availability of annotated data. Existing approaches have relied heavily on
supervised training that requires abundant annotations and pre-defined aspect
categories. Instead, with the aim of leveraging the large amount of unannotated
data now available on vaccination, we propose a novel semi-supervised approach
for vaccine attitude detection, called VADet. A variational autoencoding
architecture based on language models is employed to learn from unlabelled data
the topical information of the domain. Then, the model is fine-tuned with a few
manually annotated examples of user attitudes. We validate the effectiveness of
VADet on our annotated data and also on an existing vaccination corpus
annotated with opinions on vaccines. Our results show that VADet is able to
learn disentangled stance and aspect topics, and outperforms existing
aspect-based sentiment analysis models on both stance detection and tweet
clustering.

本文提出了一种新的半监督方法 VADet 用于检测疫苗态度，该方法通过主题建模学习无标记数据的主题信息，再用少量手动注释的样本进行微调。实验结果表明，该方法能够学习到立场和方面主题，且在立场检测和推文聚类方面优于现有的基于方面的情感分析模型。