## 2 Related Works  
There have been several works related to analyzing the Twitter dataset on different topics during the COVID-19 pandemic [12; 13; 14; 15]. Only a few studies focus on the Twitter data related to COVID-19 vaccination [16; 17]. [PERSON] et al. [12] performed text mining to identify addiction concerns during the COVID-19 pandemic. They captured public tweets containing the two keywords \"addiction\" and \"covid\" together and came up with 14 prevalent topics and provided discussion on those topics. Their dataset includes only 3301 tweets. They aim at identifying the public discussion on addiction on Twitter during the COVID pandemic but have not focused on sentiment analysis on addiction due to the pandemic. In [13], the authors worked with Twitter data related to \"Mask\". They found that the volume and polarity of mask related tweets has greatly increased during the timeline from 17 March 2020 to 27 July 2020. They also employed clustering techniques to organize these tweets into fifteen high-level themes and fifteen specific topics within each theme. They performed sentiment analysis on each theme and topic. They also applied an abstractive text summarization model using NLP to automatically interpret and describe the subject of the conversation occurring within each theme and topic cluster. [PERSON] et al. [18] used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected four million tweets on COVID-19 using 25 different hashtags in the period of 1 March 2020 to 21 April 2020. They used the NRC Emotion Lexicon to classify the sentiments into eight primary emotions: anger, anticipation, fear, surprise, sadness, joy, disgust, and trust. Their result show that Twitter users reveal fear when tweeting about COVID-19 new cases or death rather than trust. [PERSON] and [PERSON] [14] presented sentiment analysis on tweets related to bitcoins during COVID-19 pandemic using VADER. They compared 13 different text preprocessing strategies for correlating the sentiment scores of the tweets with bitcoin price. [PERSON] et al. [15] performed sentiment analysis using TextBlob on online education by webscraping 154 articles from online news and blogging websites during the COVID-19 pandemic. Their results show that over 90% of the articles are positive, and, in general, the blogs have been more positive than the newspaper articles. [PERSON] and [PERSON] [19] were the first to analyze vaccine related images on Twitter. The goal was the identification of propagation of images being used in vaccine-related tweets and to predict with a logistic regression model if the image has been retweeted or not. The authors released the labeled dataset that can be used as sentiment classifier for images. [PERSON] et al. [16] based their study on sentiment analysis of COVID-19 vaccination tweets in the Philippines. The authors used Naive Bayes model to classify English and Filipino language tweets (993 tweets) using the RapidMiner data science software with 81.77% accuracy and showed the sentiments of Filipinos towards COVID-19 vaccines. [PERSON] et al. [17] recently analyzed whether people are in favor of receiving a COVID-19 vaccine. Their result shows that on average people have weakly positive sentiment in favor of receiving the COVID-19 vaccine shots. However, the authors in their analysis used a very limited number of tweets, only 900 tweets. They did not disclose how they selected those tweets or what things they considered while scraping them. The timeline of scraping the tweets is also not mentioned in the article. Only the works in [16; 17] are somehow related to our work. However, the work of [PERSON] et al. [16] is related to tweets in the Philippines only, whereas we collect tweets around the globe. For this reason, we have around 1.2 million tweets, but they analyzed only 993 tweets.