## 2 Related Works
This work also uses the Naive Bayes model to predict the classification, whereas we classify the tweets with lexicon-based classifier and use the publicly available tools TextBlob and VADER. [PERSON] et al. [16] performed manual annotation for the training data, i.e., they manually provided the sentiment labels for the training data to predict the test data. We do not predict the sentiment labels, rather we calculate labels using the well accepted sentiment analysis tools. Thus, we cannot show any accuracy comparison with the work of [PERSON] et al. [16]. For the work given in [17], the Twitter data collection criterion and timeline are missing, which are necessary if we want to compare our result with theirs. Their dataset consists of 900 tweets only and is also not publicly available, so we could not compare our work with theirs. In our case, we do a thorough study on people's sentiment about the COVID-19 vaccines and if they are maintaining a healthy way of life after getting vaccinated. We choose seven different vaccines and collect tweets when vaccines are more accessible to the general public. Our work is reproducible given our implementation details and publicly available code-base. We show how sentiment analysis can be useful for health and government officials to lead the vaccination campaign based on people's reaction. Another contribution of this paper is related to time series forecasting of vaccinated population in the USA. Although there is a rich literature on different time series forecasting methods for many different applications [20; 21; 22], no prior work is relevant to COVID-19 vaccination forecast. The study done by the Centers for Disease Control and Prevention (CDC) [23] predicts the cumulative death for COVID-19, 4 weeks ahead using the ensemble method in R. Using this model, the CDC shows prediction for weekly death/cumulative deaths, daily hospitalization and weekly new COVID-19 cases. However, they still do not integrate any vaccination data for prediction of the vaccination scenario in the USA (state and national). This prediction takes into consideration, e.g., COVID-19 data, demographic data, and mobility data methods and estimates the impacts of interventions (social distancing, use of face coverings, etc.) and not a time series forecast. Another prediction of COVID-19 deaths and cases in the 15 countries of South and Central Europe is done in [24] using ensemble learning of the well known regression methods in WEKA. The dataset and detailed implementation are not well-described, so we cannot check how well their classifier could work on the vaccination dataset. Many other studies are focused on several other predictions: the stock market [22; 25; 26], business sales [27; 28], temperature [29], weather [30], energy consumption [31; 32], electricity [33; 34], etc. We did not find any work that shows a forecast using vaccination data in the USA. Thus, we cannot show a comparison of our model in terms of accuracy with prior works. We do a time series forecasting on the US population to show what percentage of the population will be vaccinated in the near future time. This projection will help to inform public health decisions and policy making by projecting the number in the coming weeks.