## 3 Methodology  
### Outline  
The proposed two-stage method combines unsupervised and supervised machine learning paradigms in one pipeline (Figure 1). The core of the first stage is the pre-trained zero-shot model, which is applied to (1) the emotion labels (see Table 3) and (2) the inputted text vectorized with the sentence transformer. The output of the zero-shot model is a list of emotion labels mapped to their probabilities for the input text. This output \begin{table}
\begin{tabular}{c c c c} \hline
**Paper** & **Dataset** & **Methods** & **Results** \\ \hline \multirow{5}{*}{[PERSON] et al. [16]} & \multirow{5}{*}{SIS benchmark (STSb), Korean (KorSTS), SemEval-2017 Spanish and SemEval-2017 Arabic} & SLM RoBERTa (SLM-R) that extend semantic textual similarity (STS), Machine reading comprehension (MRC), Sentiment analysis, and Alignment of sentence embeddings under various cross-lingual settings. & The Slovene language-trained model achieved the precision of 59.00 \(\pm\) 1.62 and F1-score of 52.41 \(\pm\) 2.58, when evaluated on the Croatian language dataset. \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{5}{*}{[PERSON] et al. [15]} & Slovene: SentiNews dataset and Croatian dataset & Multilingual BERT model for 3-class sentiment classification & The Slovene language-trained model achieved the precision of 59.00 \(\pm\) 1.62 and F1-score of 52.41 \(\pm\) 2.58, when evaluated on the Croatian language dataset. \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{5}{*}{[PERSON] et al. [17]} & 6 languages in Restaurant Domain: English, English, Russian, Dutch, Spanish, Turkish and French (SemEval & Two main sub-tasks of aspect-based sentiment analysis task are aspect category detection, and opinion target expression on the Dutch dataset. \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & using mBERT and XLM-R models & 91.61 \(\pm\) 0.06\% when trained on English and tested on Czech and 93.98 \(\pm\) 0.10\% when trained on English and tested on English \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{5}{*}{[PERSON] et al. [18]} & SemEval 2017 dataset Task 4 (3-class- Positive, Negative and Neutral) and two Hindi movie and Product reviews & Fine-tuned XLM-RoBERTa model & Cross lingual contextual word embedding and zero-shot transfer learning in projection prediction from resource-rich English to resource-poor Hindi language achieved 60.93\% accuracy. \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{5}{*}{[PERSON] et al. [13]} & \multirow{5}{*}{9 emotion labels: sadness, joy, anger, disgust, fear, surprise, shame, guilt and love.} & Unsupervised lexicon-based learning. & \multirow{5}{*}{F1-score is 14.20 (Top-k based), and 16.30} \\  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{5}{*}{[PERSON] et al. [47]} & SemEval 2016 Task-5. & Multilayer CNN for the sequence tagging model. Trained in one language and tested in another language that shares a common vector space. & F1-score for the zero-shot cross-language (from English to Spanish) learning from a single source to a target is 0.5 \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{5}{*}{[PERSON] et al. [48]} & \multirow{5}{*}{NepCOV19 Tweets (3-class: positive, neutral and negative)} & Ensemble CNN of three CNN models & \multirow{5}{*}{The ensemble of the three CNN models} \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of analyzed related works. The papers are compared according to the dataset, methods and results achieved.