## 7 Conclusions  
In this study, we describe an NLP-based framework for the task of analysis of COVID-19-related communication on the Twitter in Croatia. For that purpose we developed language resources for the Croatian language intended for the representation and analysis of COVID-19 tweets. We applied the proposed framework on a dataset of 206,196 COVID-19 tweets in the Croatian language posted between 1 January 2020 and 31 May 2021 (Cro-CoV-Tweets). Overall results can be summarized as follows. Negative sentiment is present in 46.8% of the COVID-19-related tweets. The negativity of tweets varies over the three pandemic waves, while the number of tweets across the 10 identified thematic clusters does not substantially vary across the three pandemic waves. The cluster with the highest number of tweets (almost 30%) is \"Public discussion regarding anti-pandemic policies and vaccines\", which is also ranked as the second most negative cluster. The most negative cluster is related to the theme \"Revolt against the COVID-19 policies and behaviour of citizens\". The highest number of retweets is present in the cluster with the theme \"Education and implementation of the COVID-19 policies\", which is ranked as the third most non-negative cluster. In terms of retweeting, we can notice that both sentiments are retweeted to an equal extent. This research demonstrates the possibilities afforded by the convenience and usefulness of NLP methods which can process a large amount of textual data and provide insights into the sentiment and topics of the observed texts. In this way, natural language processing can complement the achievements of traditional approaches used in research in the domains of humanities and social sciences when it comes to the task of analysing the public opinion and attitudes toward various COVID-19-related themes. Possible future extensions of this work include further development of the Cro-CoV-cseBERT model in terms of fine-tuning for the supervised task of sentiment analysis (Cro-CoV-cseBERT) and some other experiments in which we plan to combine embeddings from heterogeneous sources (text, metadata and network properties) for tweet representation. Moreover, we plan to use other NLP techniques such as topic modelling and named entity recognition for crisis communication monitoring. We believe our work contributes to the pursuit of the expanding social media research when it comes to the task of monitoring online communication regarding the COVID-19 pandemic. Conceptualization, A.M.; methodology, A.M., K.B., S.M.-I., M.M., S.B., M.P. ; software, K.B. ; evaluation, K.B. ; datasets construction M.P. and S.B. ; annotation--first round, M.M., S.B., S.M.-I., M.P., K.B., A.M.; annotation--supervision, M.M. ; visualization, M.P. and K.B. ; interpretation, A.M., K.B., S.M.-I., M.M., S.B., M.P. ; writing--original draft, A.M., K.B., M.P. ; writing--review and editing A.M., S.M.-I., M.M., S.B. supervision, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript. This work has been supported in part by the Croatian Science Foundation under the project IP-CORONA-04-2061, \"Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis\" (InfoCoV), and by University of Rijeka project number uniri-drusty-18-38. Data Availability Statement: The Croatian Twitter dataset related to COVID-19 topics, Cro-CoV-Tweets, and the Croatian Twitter dataset with sentiment annotations, Senti-Cro-CoV-Tweets, are available on GitHub [[https://github.com/InfoCoV/InfoCoV](https://github.com/InfoCoV/InfoCoV)]([https://github.com/InfoCoV/InfoCoV](https://github.com/InfoCoV/InfoCoV)). The Cro-CoV-cseBERT model is available on Hugging Face [[https://huggingface.co/InfoCoV/Cro-CoV-cseBERT](https://huggingface.co/InfoCoV/Cro-CoV-cseBERT)]([https://huggingface.co/InfoCoV/Cro-CoV-cseBERT](https://huggingface.co/InfoCoV/Cro-CoV-cseBERT)).