## 2 Background  
### NLP-Based Analyses of COVID-19 Related Tweets  
During the COVID-19 pandemic, a number of studies focused on the analysis of tweets related to the coronavirus outbreak. Many of these studies used various NLP techniques in different tasks, such as analysis of infodemic and information spreading in general [22; 23; 24; 25], fake news detection [26; 27], and sentiment analysis [6; 7; 8; 9; 10; 11]. Within the scope of this work, we will focus on sentiment analysis studies. The majority of studies analysed tweets posted during the early stage of the pandemic. Thus, [PERSON] et al. in [9] analysed sentiment, themes and topics of English-language COVID-19-related tweets posted in the time period between 1 January and 9 May 2020. The study explores the trends and variations in the change of the nature of COVID-19-related tweets over a period of time, from before until after the outbreak was declared a pandemic. The results show that sentiment scores are mostly negative for the topics related to the spread and increase in the number of cases. [PERSON] et al. [28] trained two classification models (naive Bayes and Logistic regression) and compared their performance on classification of COVID-19 tweets into a positive or a negative class. They found that there was a growth of negative sentiment in COVID-19 tweets. [PERSON] and [PERSON] [11] performed sentiment analysis, topic modelling and named entity recognition of tweets and news articles about COVID-19 published during the first wave of the pandemic by more than one million users from Brazil. They reported that the social media tended to have a more negative than positive and neutral sentiment, especially toward political themes. Some authors also included emotion analysis. Thus, [PERSON] et al. [8] examined trends of four emotions: fear, anger, sadness, and joy, present in worldwide tweets related to coronavirus during the early stage of the pandemic. Their findings indicate that negative emotions were dominant in the first stage of the pandemic. Similarly, [PERSON] et al. in [6] analysed emotions of 1.9 million COVID-19-related tweets in the English language, posted between 23 January and 7 March 2020. They found that the fear of the unknown nature of the coronavirus was dominant in all topics. The same group of authors [7] analysedemotions in tweets published from March to April 2020 using an emotion lexicon. They also performed topic modelling and showed that emotion of fear arises in messages related to new cases or death reports. In our previous preliminary research related to analysis of COVID-19 texts, we compared sentiment of COVID-19-related tweets in the Croatian and Polish language during the first wave of pandemic [29], detected topics in COVID-19-related news articles and comments [30], examined retweeting of COVID-19 tweets [31], and analysed the polarity of Croatian online news related to COVID-19 [32]. While the majority of studies covered sentiment analysis of COVID-19 tweets in general, some studies put focus on more specific topics, such as vaccination [33; 34] or online education [35; 36]. There is also a small number of studies that focused more on the comparison of different algorithms for sentiment classification of COVID-19 tweets than on their application and analysis of results. One such paper is [37], where the authors compared the performance of five ML algorithms on the task of sentiment analysis of a small dataset of COVID-19 tweets using different experimental settings. Additionally, they evaluated LSTM and confirmed that deep learning models do not perform well on small datasets. A large number of the mentioned studies used sentiment lexicons such as VADER or textBlob for the task of sentiment analysis, or at least, for the initial annotation of the dataset that is later used for supervised classification. Using sentiment lexicons or emotion lexicons is the faster method when it comes to obtaining results. However, some studies show that machine learning outperforms lexicon-based methods [38].