## 6 Discussion  
### Principal Results  
Principal results of this research can be divided into two parts: (i) the Cro-CoV-cseBERT-based framework developed for the COVID-19 tweets analysis described in Sections 3 and 4, and (ii) the results of the application of the framework on the dataset of COVID-19 tweets in the Croatian language described in Section 5. The Cro-CoV-cseBERT-based framework is a prerequisite for an extensive analysis of COVID-19 tweets that enables answering the three research questions. The Cro-CoV-cseBERT-based framework was developed for the task of analysis of COVID-19-related communication on Twitter in Croatia. For that purpose, we developed language resources for the Croatian language intended for the representation and analysis of COVID-19 tweets. Within the proposed framework, we used the existing NLP methods for the task of sentiment analysis and clustering. That is in line with some other similar studies that use similar approaches for other languages [6; 7; 8; 9; 10; 11]. However, in order to deal with the texts in the Croatian language related to the domain of COVID-19, we have to develop adequate language resources. Thus, in response to other similar studies, this research contributes in terms of (i) Cro-CoV-cseBERT language model, (ii) a dataset of 206,196 unique COVID-19 tweets in the Croatian language posted between 1 January 2020 and 31 May 2021--Cro-CoV-Tweets dataset, and (iii) a dataset of 10,000 tweets annotated manually with one of the three labels describing the sentiment (positive, negative, neutral)--Senti-Cro-CoV-Tweets dataset. In the second part of the research, we performed an extensive analysis of COVID-19-related tweets in the Croatian language. Specifically, we addressed the three open research questions and our main findings are summarized below. Regarding the first research question related to sentiment, we found that negative sentiment is present in 46.8% of the COVID-19-related tweets. The negativity of tweets varies substantially over the three pandemic waves. The amount of negative sentiment in tweets is highest during the first wave, during the lockdown period. Later, the higher number of negative tweets is exhibited during August 2020, probably due to the sudden and early end of the tourist season (note that tourism is one of the main economic sectors in Croatia). Again, one negative peak appeared in April 2021 caused by the uncertainty brought about by the third epidemic wave. Less negative tweets are present in May /June 2020 and May 2021 due to the decrease in the number of confirmed COVID-19 cases. These results confirm our previous findings in [29], where we have presented similar results for tweets in Croatian and Polish posted during the first wave of the pandemic. Our findings are also in line with similar studies for other languages which have already revealed that negative attitudes and emotions are dominant in tweets posted during the COVID-19 pandemic, such as [6; 7; 8; 9; 11; 28]. The second research question is related to the main themes present in the COVID-19 tweets and how these trends change over time. The cluster with the highest number of tweets (29.63%) is associated with the theme \"Public discussion regarding anti-pandemic policies and vaccines\". It is followed by the clusters with themes related to \"Coping with the pandemic\" (18.37%) and \"Public comments on statements of the politicians and scientists\" (15.10%). The number of tweets in clusters is more consistent over time than sentiment is. Greatest changes are present in the cluster of tweets related to the \"Messages on how to cope with the pandemic\" that had more tweets in the first than in the second and the third waves. This makes sense because this topic was the focal point when it came to providing important information when the pandemic first started. The cluster related to the theme \"Public comments on statements of the politicians and scientists\" has a higher number of tweets in the second and third waves than in the first wave.