## 4 Results
Moreover, to clarify the good performance of BOSentiment and BoSA, we have also calculated the confusion matrix (A confusion matrix is a tool used to evaluate the performance of a classification model. It shows the number of correct and incorrect predictions made by the model compared to the actual outcomes. The matrix is usually represented as a table where each row represents the true labels and each column represents the predicted labels.). Looking at the confusion matrices provided in Figure 5, we can see that the diagonal elements of the matrix (the true positive predictions) have higher values compared to the off-diagonal elements (the false positive and false negative predictions). This indicates that the models performed well in correctly identifying the true class labels. For example, in the case of the BoSA classifier, we can see that the diagonal elements have high values, particularly for the first and second classes (represented in the rows and columns). This suggests that the model was able to correctly classify a large number of instances belonging to these classes. However, we can also see that there are some misclassifications, particularly in the third class, which may require further investigation. Similarly, for the BOSentiment classifier, we see high values along the diagonal elements for the first and third classes. This indicates that the model was successful in correctly predicting a large number of instances belonging to these classes. However, there are some misclassifications in the second class, which may require further investigation. Overall, the confusion matrices provide valuable insights into the performance of the classification models and can help guide further improvements to the models. Having that in mind, we conducted an overall annotation using the BOSentiment model, which was processed with the BoSA model. First, we used the BoSA model to extract the sentiment values of the tweets. Then, those values were used as features for  
Figure 5: Visualization of the performance of algorithms in the confusion matrix. The output matrix has six cells, diagonal elements present a number of elements that were classified as correct for the given sentiment value, and all other presents a number of elements that were not classified as correct. the BOSentiment model. As can be seen from the lower part of Figure 6, the left side consists of a number of tweets that were labeled by humans (238 negatives, 106 neutrals, and 73 positives). By processing these tweets with the BoSA model, we calculated the sentiment values of the tweets, and we got correctly classified 215 negative tweets, 77 neutral tweets, and 35 positive tweets. Those sentiment values were then processed with the BOSentiment model which gave us correctly classified 221 negative tweets, 95 neutral tweets, and 56 positive tweets. On the top of Figure 6 in the scatter can be seen a comparison between human annotation and the BOSentiment model. Moreover, as can be seen from Table 3, we have \(92,86\%\) negative tweets that were correctly classified as negative with the BOSentiment classifier, \(89.62\%\) that remained neutral after classification, and \(76.71\%\) that were classified correctly as positive. In order to further reinforce the accuracy of our model, we utilized Neural Networks, specifically Convolution neural networks (CNN). As mentioned earlier, our data-set included sentiment data (negative, positive, and neutral), so, we encoded each sentence with three labels, with one value representing the actual label. For instance, \"Negative\" was represented as \([1,0,0]\), \"Neutral\" as \([0,1,0]\), and \"Positive\" as \([0,0,1]\). As can be seen from Listing 1, we have used seven layers in our pure CNN model. After applying Embedding and the Conv1D layer, we get a new matrix of dimensions \((3,28,64)\). This matrix is obtained by interpreting our initial matrix of dimensions \((3,32)\) as 3 rows, each of length 32, and then applying a filter of dimensions \((5,32)\) on each of these rows using 64 filters.