## 6 Results
### Discussion  
First of all, the model shows non-zero precision and recall for all emotion categories, as we can see in Table 2 data. As mentioned before, the dataset is imbalanced, and some of the classes used have 0 precision and recall, meaning that the amount of data was not enough to fit the model. This issue was resolved by applying oversampling for low emotional categories on the training dataset (Section 4.2). \begin{table}
\begin{tabular}{c c c} \hline
**Emotion** & **Precision** & **Recall** \\ \hline admiration & 0.68873 & 0.73473 \\ \hline amusement & 0.76567 & 0.88365 \\ \hline anger & 0.47297 & 0.52239 \\ \hline annoyance & 0.31169 & 0.32432 \\ \hline approval & 0.49838 & 0.39386 \\ \hline caring & 0.51351 & 0.42222 \\ \hline confusion & 0.43307 & 0.31609 \\ \hline curiosity & 0.55866 & 0.71174 \\ \hline desire & 0.75676 & 0.35443 \\ \hline disappointment & 0.31034 & 0.0625 \\ \hline disapproval & 0.52717 & 0.33333 \\ \hline disgust & 0.81395 & 0.35354 \\ \hline embarrassment & 0.72 & 0.46154 \\ \hline excitement & 0.7619 & 0.16495 \\ \hline fear & 0.71186 & 0.48276 \\ \hline gratitude & 0.91268 & 0.90251 \\ \hline grief & 0.33333 & 0.625 \\ \hline joy & 0.63448 & 0.55758 \\ \hline love & 0.71765 & 0.9037 \\ \hline nervousness & 0.33333 & 0.35294 \\ \hline optimism & 0.57065 & 0.61404 \\ \hline pride & 0.53333 & 0.72727 \\ \hline realization & 0.63889 & 0.18254 \\ \hline relief & 0.16667 & 0.17647 \\ \hline remorse & 0.64935 & 0.75758 \\ \hline sadness & 0.56 & 0.5283 \\ \hline surprise & 0.60976 & 0.53191 \\ \hline \end{tabular}
\end{table} 
Table 3: Precision and Recall metrics for each emotion category reached on the testing dataset (source: author’s development). The main metrics to compare models during the experiments were micro- and macro-averaged F1-Scores, giving priority to the macro-averaged score, as it works well on imbalanced data and treats all classes equally. The all-vs-all confusion matrix, which is shown in Appendix D, represents a clear diagonal of correct predictions. This supports the previous conclusion that the model can distinguish and predict all emotion categories. It is worth mentioning that the model sometimes confuses semantically close classes. As we can see, anger and annoyance, curiosity and confusion have significant confusion. This can be seen in the all-vs-all confusion matrix. The reason for this is that similar language can be used for expressing these emotions. For instance, aggressive words can be frequently used in the expression of both anger and annoyance, while question forms should be common for curiosity and confusion emotions. As for the ability of the model to distinguish positive, negative, and ambiguous sentiments, higher F1-Scores (micro- and macro-averaged) show that it was achieved. This also supports the idea that even though sometimes it can be hard for the model to differ one emotion from another if they have close semantics, the model can determine whether positive, negative, or ambiguous emotions are expressed. The achieved results give much room for further research by applying the emotion classification model. Apart from the exact data science, the model can also be used for various interdisciplinary studies. These can be in any area that involves human behavior. For instance, conducting marketing analysis and sociology research might require an examination of the emotions and sentiments of the target audience. The model can predict emotions expressed in text questionnaires and provide a source for the data mining process. The quality of prediction significantly depends on the amount of data for this emotion category. This is a common rule for the machine learning industry and is supported by this study. As we can see from the all-vs-all confusion matrix presented in Appendix D, the classifier predicted more accurately classes with a higher amount of data: admiration, amusement, gratitude, and love. At the same time, it made less accurate predictions for embarrassment, relief, excitement, and remorse, which have comparably fewer labels.