Objective To develop and apply a natural language processing (NLP) – based approach to analyze public sentiments on social media and their geographic pattern in the United States toward COVID-19 vaccination. We also provide insights to facilitate the understanding of the public attitudes and concerns regarding COVID-19 vaccination.

Methods We collected Tweet posts by the residents in the United States after the official dissemination of the COVID-19 vaccine. We performed sentiment analysis based on the Bidirectional Encoder Representations from Transformers (BERT) and qualitative content analysis. Time series models were leveraged to describe sentiment trends. Key topics were analyzed longitudinally and geospatially.

Results A total of 3,198,686 Tweets related to COVID-19 vaccination were extracted from January 2021 to February 2022. 2,358,783 Tweets were identified to contain clear opinions, among which 824,755 (35.0%) expressed negative opinions towards vaccination while 1,534,028 (65.0%) demonstrated positive opinions. The accuracy of the BERT model was 79.67%. The key hashtag-based topics include Pfizer, breaking, wearamask, and smartnews. The sentiment towards vaccination across the states showed manifest variability. Key barriers to vaccination include mistrust, hesitancy, safety concern, misinformation, and inequity.

Conclusion We found that opinions toward the COVID-19 vaccination varied across different places and over time. This study demonstrates the potential of an analytical pipeline, which integrates NLP-enabled modeling, time series, and geospatial analyses of social media data. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccination, help address the concerns of vaccine skeptics, and provide support for developing tailored policies and communication strategies to maximize uptake.

The authors have declared no competing interest.

This study did not receive any funding.

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