## 6 Results
### Comparison with a Baseline Approach  
Authors of the GoEmotions dataset paper validated if the dataset is suitable for modeling purposes in the \"6. Transfer Learning Experiments section\" [7]. They presented results of baseline fine-tuning of the pre-trained BERT-base model (12 layers and 768 hidden units). The baseline model showed a macro-averaged score of 0.46 in emotions prediction and 0.69 in sentiments prediction. The authors of this study describe a more complex model with better text pre-processing and dataset preparation. The authors reached a macro-averaged F1-Score of 0.50704 in emotions prediction and 0.7349 in sentiments prediction. Thus, the authors increased the quality of emotions prediction by 10.2% and sentiments prediction by 6.5%. It is important to mention that, in this study, the dataset has a different train-test split. Consequently, this is not just the classification model that performs better but the method in general that achieves higher metrics on unseen data. The main improvement of the existing method is having an additional text standardization layer and different model configurations, which leads to achieving higher metrics on the testing dataset. For direct comparison, the model was trained and validated on the original GoEmotions dataset without changes to splits. The neutral class is used \"as is\", although, in the writers' opinion, it should be excluded. Table 4 shows metrics reached in emotions prediction, while Table 5 presents sentiments prediction results. The model shows zero precision and recall metrics for classes \"embarrassment\", \"grief\", \"nervousness\", \"pride\", and \"relief\". These scores are lower than those presented in the original GoEmotions study. This can be explained by using only a tiny-BERT model (2 layers, 128 hidden units) due to computing resource limitations. Employing a more complex BERT model, for instance, BERT-base, should positively impact the classification quality. As mentioned before, the GoEmotions research refers to the BERT-base model as a baseline. Another factor to consider is different splits. Re-splitting the dataset with fraction by class, as well as oversampling low classes, helps the model detect and distinguish all emotion categories. Finally, excluding the neutral class leads to increasing the model's performance. The GoEmotions research describes it as: \"If raters were not certain about any emotion being expressed, they were asked to select Neutral. We included a checkbox for raters to indicate \begin{table}
\begin{tabular}{c c} \hline \hline
**F1-Score (Micro)** & **F1-Score (Macro)** \\ \hline
0.4684 & 0.3442 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: F1-Score micro- and macro-averaged metrics for all classes reached on the testing dataset on original GoEmotions data in emotions prediction (source: author’s development). \begin{table}
\begin{tabular}{c c} \hline \hline
**F1-Score (Micro)** & **F1-Score (Macro)** \\ \hline
0.6711 & 0.6367 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F1-Score micro- and macro-averaged metrics for all classes reached on the testing dataset on original GoEmotions data in sentiments prediction (source: author’s development). if an example was particularly difficult to label, in which case they could select no emotions. We removed all examples for which no emotion was selected\" [7]. We question this approach as the emotions are either expressed in a piece of text or not. In case no emotions are expressed, the model should not trigger any emotion categories by threshold. Apart from that, the neutral class, being the largest one, causes model overfitting toward itself. Thus, the neutral class is excluded from the current study. For supplementary comparison, the approach proposed in this study was limited to training the vanilla tiny-BERT model (2 layers, 128 hidden units) without text pre-processing and additional layers on the presented splits. Although hyper-parameter tuning might be required, these were used as presented before (Section 5.2 Train Process).