The Covid-19 pandemic that hit the world, including in Indonesia, had a significant impact. Casualties, economic downturn, extreme poverty, and major changes in education are still happening today. The presence of the Covid19 vaccine is new hope for mankind to end this pandemic situation. The emergence of two types of vaccines in Indonesia, Sinovac, and Pfizer, lead to different Indonesian society reactions. This study aims to do a sentiment analysis of the two types of vaccines on the Twitter platform. Data from October until November 2020 has been crawled and processed to see the citizen opinion. The dataset was split into two types: Sinovac and Pfizer dataset. Both datasets were labeled manually into three classes: positive, negative, and neutral. The results show that 77% of Tweets indicate the positive segments, while 19% represent negative, and 4% seem to be neutral for Sinovac. From the standpoint of Pfizer, the results were 81%, 17%, and 3% for positive, negative, and neutral, respectively. In terms of model performance evaluation, with 10-fold cross-validation, the highest average accuracy in the Sinovac dataset is Support Vector Machine with 85% accuracy. Furthermore, the Support Vector Machine classifier has a superior accuracy value of 78% in the Pfizer dataset compared to other classifiers.