[Objective] Topic mining and multi-label classification are leveraged to provide decision support for vaccine adverse event (VAE) monitoring and public opinion analysis.

[Methods] We propose a latent Dirichlet allocation-based VAE topic modeling method with the use of domain knowledge and accordingly develop a public opinion analysis method for vaccine-associated posts based on different strategies of multi-label classification. Finally, we discuss the relationships between user vaccine-related sentiments and the patterns of online user behaviors.

[Results] The use of sentiment dictionaries and MedDRA terminology sets improves the accuracy of VAE-related sentiment analysis by up to 15.17%. The One-vs-Rest-based methods achieve an accuracy of up to 97.15% while the other methods merely achieve an average accuracy of 80%.

[Limitations] Lots of non-standard terms about VAE-associated posts on social media have great influence in vaccine-related information extraction. Further use of controlled medical terminology and multimodal information analysis will improve the accuracy of vaccine-related public opinion analysis.

[Conclusions] VAE topic mining and sentiment analysis improve the accuracy of public opinion analysis and decision support for people after massive vaccination.