# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 4 Related Work
### Sentiment Analysis in Slavic Languages  
The [PERSON] and [PERSON] [51] dataset of film reviews with fine-grained scores was a significant contribution to Bulgarian sentiment analysis. [PERSON] et al. [52] compiled a dataset containing customer feedback derived from online store reviews. [PERSON] and [PERSON] [53] classified film reviews using a semi-supervised multi-view genetic algorithm. [PERSON] and [PERSON] [54] described the creation of a corpus of Bulgarian political speech. A classification of Bulgarian tweets was performed by [PERSON] et al. [55]. [PERSON] [56] provides a concise overview of the text-analytic work in Bulgarian.  
[PERSON] et al. [57] created sentiment dictionaries for multiple languages, including Czech, that are multilingual and comparable. [PERSON] [58] compiled a corpus of annotated opinion articles from the Aktualne.cz news website. This was supplemented with supplementary data derived from domestic appliance reviews on the Mall.cz retail website. A dataset of Czech film reviews was compiled by [PERSON] and [PERSON] [59]. The authors iteratively examined the Maximum Entropy classifier and Gibb's sampling to determine the desired probabilities. [PERSON] and [PERSON] [60] evaluated supervised machine learning algorithms using the Mall.cz and Facebook datasets. BERT-based models for Czech sentiment have also been attempted [61; 62; 63; 64]).  
[PERSON] et al. [65] developed grammar-based rules for determining the overall sentiment of Croatian financial news. [PERSON] and [PERSON] [66] have created rule-based techniques for detecting sentiment in horoscopes published on news portal websites. [PERSON] and [PERSON] [67] evaluated a lexicon-based method for analyzing user comments in the transportation domain. [PERSON] et al. [68] presented aspect-based, domain-specific sentiment analysis for the Croatian language. [PERSON] et al. [69] and [PERSON] and [PERSON] [70] have studied the sentiment analysis of Croatian social media text. [PERSON] et al. [71] compared the Slavic and Germanic language families for a Twitter sentiment analysis task. [PERSON] and [PERSON] [72] discussed theoretical and practical aspects of Polish consumer opinions. [PERSON] et al. [73] presented the first attempt to create a polarity lexicon that is accessible to the public. They utilized readily available resources such as dictionaries, thesauri, and existing open-source initiatives. Other attempts at solving SA in Polish primarily include lexicons [74], Wordnet features [75], and unigrams/bigrams [76]. Numerous authors, such as [PERSON] et al. [77], [PERSON] and [PERSON] [78], have compared and contrasted machine learning and deep learning techniques for sentiment recognition, including naive Bayes, SVM, BiLSTM, and BERT.  
Rules [79], machine learning techniques [80], and deep learning approaches have been described in previous work on the Russian language. Using various neural techniques, [PERSON] and [PERSON] [81] improved the scores on multiple Russian sentiment datasets. This work proposed sentiment classification as a task of natural language inference and improved final scores. [PERSON] and [PERSON] [82] continued the same work with three-step sequential training and achieved state-of-the-art results. [PERSON] and [PERSON] [83] identified multiple datasets and baselines for a sentiment analysis task in Russian. [PERSON] et al. [84] translated an English lexicon into Slovak and combined it with a particle swarm optimization algorithm to construct a lexicon-based sentiment categorization system. [PERSON] et al. [85] annotated and evaluated five distinct classifiers for Slovenian web media content. Various attempts have been made at sentiment analysis in Slovenian news texts [86; 87; 88; 89]. The corpus of web commentary was examined by [PERSON] and [PERSON] [90]. Offensive language detection in Slovene [91; 92] is an active area of research.