## 2 Related Work  
Sentiment analysis is among the principal tasks of NLP that strives to predict opinion polarity. It often predicts the sentiment as belonging to one of the three categories (negative, neutral and positive) that can be used in many areas such as customer product review [35], political forecasting [11], telehealth services [36], finance [37], etc. [38].  
According to [PERSON] et al. [39], we describe the taxonomy of sentiment analysis techniques and divide it into two main paradigms: rule-/lexicon-based and machine learning. Lexicon-based methods [40] rely on the assumption that the overall sentiment depends on the words that explicitly express these sentiments. Words (adjectives, adverbs, sometimes verbs, and nouns) that define different sentiments are searched in the text and counted: the overall sentiment of the text depends on the majority. In machine learning, the sentiment analysis task is typically formulated as the text classification task and, therefore, can be solved with a whole spectrum of methods for this purpose: traditional machine learning methods (e.g., Support Vector Machine (SVM)), deep learning methods (e.g., Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)), or innovative sentence transformer models (e.g., Bidirectional Encoder Representation from Transformer (BERT)).  
For many years different languages were treated separately by training monolingual models from scratch for separate tasks and separate languages [41, 42]. Recently, many pre-trained models provided by open-source NLP libraries, such as BERT and NTLK, were introduced to minimize the efforts and resources required to learn general knowledge about the language and its structure (i.e., existing words, their meanings, and similarities). These transformer models are typically trained on very large monolingual or multilingual unannotated corpora (i.e., on pure texts) in a self-supervised manner and therefore are not adjusted for specific NLP problems [43]. With the help of transfer learning, the previously acquired general knowledge in the pre-trained word- or sentence-transformer models can be augmented and fine-tuned to tackle the specific NLP problems (including the sentiment analysis task). Once the model is already 'familiar' with the language, it is much easier to adapt it to a specific NLP task: that is, typically, fewer training data are needed. Moreover, some multilingual transformer models are trained on the parallel corpora and tuned for similar tasks in the way they can cross barriers between languages. Cross-lingual methods have recently received more NLP community attention, thus demonstrating promising results when fine-tuning augmented transformer layers on different languages than they are later tested on (a good example is a group of the cross-lingual language model (XLM) transformers) [44].  
Pre-trained transformer models can be used for sentiment analysis tasks in very different manners [45; 46]: as text classifiers (by adding additional layers connecting the output of the transformer model with the sentiment labels); for the evaluation (by calculating distances between the unseen texts and texts of which the sentiments are already known); as zero-shot models that can evaluate the relatedness of some word (category, narrative, etc.) with the text. Zero-shot models can act as advanced dictionary-based methods that seek emotion or sentiment words both explicitly and implicitly and return probabilities determining how much these words are related to the text. The zero-shot models do not require the training data, but they are not directly adjusted for the sentiment analysis tasks and, therefore, may need additional mechanisms to go their limitations. In Table 2, we summarize the sentiment analysis methods that are the most influential in solving our problem.