Background COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods We collected 31,100 English tweets containing COVID-19 vaccine–related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

BACKGROUND The novel coronavirus disease (COVID-19) is one of the greatest threats to human beings in terms of healthcare, economy and society in recent history. Up to this moment, there are no signs of remission and there is no proven effective cure. The vaccine is the primary biomedical preventive measure against the novel coronavirus. However, the public bias or sentiments, as reflected on social media, may have a significant impact on the progress to achieve the herd immunity needed principally. OBJECTIVE This study aims to use machine learning methods to extract public topics and sentiments on the COVID-19 vaccination on Twitter. METHODS We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed the tweets by visualizing the high-frequency word clouds and correlations between word tokens. We built the Latent Dirichlet Allocation (LDA) topic model to identify the commonly discussed topics from massive tweets. We also performed sentiment analysis to understand the overall sentiments and emotions on COVID-19 vaccination in Australian society. RESULTS Our analysis identified three LDA topics, including "Attitudes towards COVID-19 and its vaccination", "Advocating infection control measures against COVID-19", and "Misconceptions and complaints about COVID-19 control". In all tweets, nearly two-thirds of the sentiments were positive, and around one-third were negative in the public opinion about the COVID-19 vaccine. Among the eight basic emotions, "trust" and "anticipation" were the two prominent positive emotions, while "fear" was the top negative emotion in the tweets. CONCLUSIONS Our new findings indicate that some Australian Twitter users supported infection control measures against COVID-19 and would refute misinformation. However, the others who underestimated the risks and severity of COVID-19 would probably rationalize their position on the COVID-19 vaccine with certain conspiracy theories. It is also noticed that the level of positive sentiment in the public may not be enough to further a vaccination coverage which would be sufficient to achieve vaccination-induced herd immunity. Governments should explore the public opinion and sentiments towards COVID-19 and its vaccination and implement an effective vaccination promotion scheme besides supporting the development and clinical administration of COVID-19 vaccines.