After Declaring pandemic in March 2020 public health prevention measures have proven to be somewhat effective in limiting the spread of COVID-19. Protective immunity through vaccination will be great importance of in ending the pandemic. This work aims to identify the sentiments of the masses towards vaccination by analyzing the text tweets. 68,654 tweets are retrieved from Twitter posted within the timeline from December 2020 to January 2021. Sentiment, polarity score, and subjectivity score were computed and analyzed on the basis of text and date columns. According to the polarity and subjectivity scores, tweets were classified as positive, negative, and neutral using the TextBlob library of natural language processing. Also, the user’s view on the vaccination was analyzed using machine learning algorithms such as Naïve Bayes (NB) and Logistic Regression (LR). The highest accuracy achieved was 90% by Logistic Regression (LR). It was observed from the results that the sentiments towards the vaccine were positive on the initial day but a shift to negative sentiments was observed. Later, the sentiment towards vaccines is again positive.