## 4 Experiments and Results
### Results  
The results of experiments on zero-shot classification (first stage) are summarized in Table 4. We compared four zero-shot models (i.e., _part-large-mnli_, _Fb_improved_zeroshot_, _covid-twitter-bert-v2-mnli_, and _part-large-mnli-yahoo-answers_). The models were employed for zero-shot classification via a pipeline in the [PERSON] face's transformers package. The determined most accurate zero-shot model (i.e., _part-large-mnli_), which gives the best performance of 0.747 on the Sentiment140 dataset with the single-model machine learning classifiers, was later used in our further experiments. Table 5 represents the accuracies of single model machine learning and ensemble classifiers with different sets of emotions (from Table 3) on the SemEval-2017 dataset using three-class classification. The best overall accuracy was achieved by the stacking classifier on the first set of emotions (0.627). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & _part-large-mnli-yahoo-answers_ & _bart-large-mnli_ & _covid-twitter-bert-v2-mnli_ & _Fb_improved_zeroshot_ \\ \hline Linear regression & 0.727 & 0.740 & 0.670 & 0.693 \\ KNN & 0.650 & **0.747** & 0.740 & 0.663 \\ CART & 0.700 & 0.730 & 0.677 & 0.693 \\ SVM & 0.727 & 0.733 & 0.670 & 0.693 \\ Naive Bayes & 0.513 & 0.723 & 0.680 & 0.523 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The impact of zero-shot models on the accuracy of machine learning classifiers for the binary sentiment classification with the Sentiment140 dataset. The best result is shown in bold. Figure 5: The distribution of texts among positive/negative/neutral sentiment categories in the IMDB, Sentiment 140, and SemEval-2017 datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Methodology** & **Method** & **First Set** & **Second Set** & **Third Set** & **Fourth Set** \\ \hline \multirow{4}{*}{Single-model machine learning} & FFNN & 0.338 & 0.433 & 0.484 & 0.458 \\  & Linear regression & 0.611 & 0.546 & 0.575 & 0.516 \\  & KNN & 0.577 & 0.501 & 0.541 & 0.484 \\  & SVM & 0.611 & 0.546 & 0.575 & 0.516 \\  & Naive Bayes & 0.555 & 0.538 & 0.575 & 0.520 \\  & CART & 0.611 & 0.544 & 0.574 & 0.516 \\ \hline \multirow{4}{*}{Ensemble learning} & AdaBoost Classifier & 0.611 & 0.551 & 0.578 & 0.519 \\  & AdaBoost regressor & 0.292 & 0.256 & 0.357 & 0.219 \\ \cline{1-1}  & Bagging classifier & 0.611 & 0.551 & 0.578 & 0.519 \\ \cline{1-1}  & Bagging regressor & 0.263 & 0.266 & 0.270 & 0.207 \\ \cline{1-1}  & Extra message classifier & 0.611 & 0.551 & 0.578 & 0.519 \\ \cline{1-1}  & HistGradientBoost classifier & 0.611 & 0.551 & 0.578 & 0.519 \\ \cline{1-1}  & Stacking classifier & **0.627** & 0.544 & 0.578 & 0.509 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of classifiers on the SemEval-2017 dataset using three-class classification with different sets of emotions. The best result is shown in bold. Table 6 shows the accuracy of single-model machine learning and ensemble classifiers on the SemEval-2017 dataset (of two-class classification without the neutral class) with different sets of emotions. The best overall accuracy was also achieved by the stacking classifier on the third set of emotions (0.873). Table 7 compares the accuracy of single-model machine learning and ensemble classifiers on three analyzed datasets. The best overall accuracy was matched by the stacking classifier and FFNN on the SemEval-2017 dataset (without using the neutral class) (0.873). The experiment with single-model and ensemble learning methods shows the superiority of ensemble methods (see Tables 5 and 6). It is explainable: they combine the knowledge from several classifiers. The highest accuracy for both the binary and 3-class classification problems was achieved with the ensemble learning type methods 0.873 and 0.627, respectively, using the SemEval-2017 dataset.