## 2 State of the Art
### Sentiment Analysis in the Bosnian/Croatian/Serbian/Slovenian Language  
Lexicons have been developed for various Slavic languages, providing valuable resources for sentiment analysis in these specific linguistic contexts. For instance, lexicons have been created for the Bosnian language by [PERSON] and [PERSON] [34], Bulgarian by [PERSON] and [PERSON] et al. [35], Croatian by [PERSON] et al.[36], Czech by [PERSON] [37], Macedonian by [PERSON] et al. [38], Polish by [PERSON] [39], Slovak by [PERSON] [40], and Slovenian by [PERSON] [41].  
The field of sentiment analysis in the Bosnian, Croatian, Serbian, and Slovenian languages has been experiencing significant growth in recent times. There have been many different approaches presented in the last few years.  
To the author's knowledge, there are only two papers presenting research limited to the Bosnian language, both by [PERSON] and [PERSON], where the first article is about creating the first Bosnian lexicon [42] and the second introduces a sentiment-annotated lexicon specifically designed for the Bosnian language. The researchers evaluated the coverage of this lexicon by conducting experiments on two reference corpora [43].  
According to research from 2012, [PERSON] et al. in [44] point out that Croatian is a language very far from the optimal development of language technologies (tools and resources). As for language resources that are relevant when creating a sentiment analysis system for the Croatian language, we can talk about two resources: a corpus of sentimentally indicated Croatian news (_\"Sentiment Annotated Data-set of Croatian News\"_) ([45]) and _\"Twitter Sentiment for 15 European languages\"_ ([46]), both published on the CLARIN.SI platform.  
Moreover, [PERSON] et al. in [36] focused on the task of semi-supervised lexicon acquisition, and [PERSON] et al. in [47] gave insight into intensifiers of the Croatian language. The Serbian language has been the subject of extensive research in the field of computational linguistics, with numerous studies exploring various aspects of language processing. For instance, researchers have dedicated their efforts to constructing the Serbian wordnet [48], a valuable resource that organizes Serbian lexical units and their semantic relations. Sentiment analysis, another area of interest, has been applied to analyze the sentiment expressed in Serbian newspaper content [49] as well as movie reviews [50], and the classification of documents based on \(n-\)grams [51], and [52] music album reviews.  
[PERSON] et al. in [53] used the lexicon-based unsupervised method of sentiment analysis in the Serbian language. Moreover, [PERSON] in [54] gave a detailed analysis of negations in sentiment analysis. [PERSON] et al. [55] have made significant contributions to the field of sentiment analysis in Slovene by introducing new language resources such as corpora, annotations, and a lexicon. Their work has paved the way for further advancements in understanding sentiment in Slovene-language texts. In a related study, [PERSON] et al. [56] developed machine-learned sentiment classifiers specifically tailored for Slovene tweets. These classifiers were trained using labeled tweet data, but the findings of an empirical analysis conducted by [PERSON] et al. [57] suggest that these models show promise for classifying other text types as well, including news articles.