## 5 Discussion and Further Work  
Considering the limited research on sentiment analysis in the Bosnian language, the presented models, particularly the BOSentiment model on its own or in combination with CNN, have shown acceptable performance in accurately sentimentally labeling tweets written in Bosnian. This highlights the potential and effectiveness of our models in addressing sentiment analysis tasks in underrepresented languages. Taking all the results into consideration, it is evident that there is substantial performance variability across the different models, and there is no universal solution. However, based on the outcomes presented in the table and the graph, the 'CNNBOSf256' model emerges as the top performer with an accuracy of approximately 92%. This model surpasses other models, including the widely used CNN models, and achieves a relatively low error. Therefore, based on these findings, we consider the 'CNNBOSf256' model to be the most suitable candidate for sentiment classification tasks of the Bosnian language. Even more, the method is easily applicable at least to similar/related languages (at least all the languages from the south-Slavic language group), with possible applications to other languages.  
Furthermore, the base model BOSentiment performed well in predicting sentiment for tweets but combining it with CNN as a pre-predicted model did not result in much better accuracy if the training data and testing data were split 40:60, as shown in Table 4. This serves as additional confirmation that the BOSentiment model has a good structure and is an excellent pre-trained model for CNN.  
The future focus will be on improving the results of the presented study by upgrading the BOSentiment model and examining AnA-words in separate categories to contribute to better sentiment analysis of the Bosnian language. Additionally, the aim will be to explore  
Figure 8: Performance of 20 CNN models on training and testing data. The CNN model with 256 filters of size \(4\times 4\) stands out as the best performer with an accuracy of around 92%.  
the possibility of incorporating context-specific sentiment analysis, such as analyzing tweets based on their source or topic. Furthermore, the performance of other machine-learning algorithms, such as Support Vector Machines, Random Forests, and other neural networks, will be investigated and the results will be compared with those obtained from the current approach. Ultimately, the goal is to provide a more accurate and comprehensive sentiment analysis of the Bosnian language on social media platforms.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, original draft preparation, visualization S.J.; review and editing, supervision, funding acquisition J.V. All authors have read and agreed to the published version of the manuscript. Funding for this research was provided by European Commission through the Horizon 2020 'InnoRenew CoE' (Grant Agreement no. 739574) and SRC-EDIH--Smart, Resilient, and Sustainable Communities--European Digital Innovation Hub (Grant Agreement no. 101083351). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AnA & A word from the AnA-words list \\ AnA-words & Affirmative and non-Affirmative words \\ BCMS & Bosnian-Croatian-Montenegrin-Serbian language \\ BERT & Bidirectional Encoder Representations from Transformers \\ CNN & Convolution Neural Network \\ CRF & Conditional random fields \\ LBM & Lexicon-based method \\ NLTK & Natural Language Toolkit \\ RF & Random Forest \\ SVM & Support Vector Machine \\ \end{tabular}