## 5 Results and Discussion
### Prediction Accuracy  
The first metric used to analyze the performance of the classification based on the number of correctly classified data is the Prediction Accuracy [27], which is defined as the ratio between the number of correctly made predictions and the total number of predictions. Multiplying this ratio by 100 will result in a percentage value. We further analyze the graphical representation of accuracy for the three types of Naive Bayes algorithms: Multinomial, Gaussian, and Bernoulli. Figure 7 shows the accuracy percentage for the Multinomial classifier NB. Bases on the analysis performed, this model has the best prediction accuracy. Checking through multiple tests, the accuracy range of the classifier is between 76-82%, with values mainly in the upper area of the range. On average, we can consider that the accuracy of this classifier is approximately 80%. Compared to the current state of the art, the result obtained is acceptable, considering that related works based on neural networks reach an accuracy of 85-95%. We further analyze the accuracy for Gaussian NB and Bernoulli NB (Figures 8 and 9). Figure 7: Graphical representation of the accuracy of the Multinomial NB classifier. The accuracy range obtained for the Gaussian NB classifier is 73-76% (Figure 9), slightly lower than in the case of the NB Multinomial classifier. With an average of these accuracy values around 75%, the model based on the Gaussian NB algorithm correctly classifies at least three quarters of tweet posts. Although it does not reach the maximum performance of the Multinomial classifier, compared to the previously mentioned data in the current context, the results are satisfactory. Figure 9 shows the accuracy of the Bernoulli NB classifier. The Bernoulli classifier has the lowest accuracy of the three models (Figure 9 and has the largest number of incorrectly assigned labels. The accuracy range is 72-75%, with an average of about 73%. Thus, there are cases where this model correctly classifies just under three quarters of the total number of data. However, it should be noted that the Bernoulli algorithm is the fastest in generating results. The performance of this algorithm is lower because the algorithm is optimized for a smaller data set. However, considering all the  
Figure 8: Graphical representation of the accuracy of the Gaussian NB classifier. Figure 9: Graphical representation of the accuracy of the Bernoulli NB classifier. values obtained so far for accuracy, the Bernoulli algorithm can also be used to result in acceptable values. We further analyze the accuracy according to the number of correctly labeled posts. Figure 10 shows the Train-Test Split, using an 80-20 ratio. The number of correctly labeled posts decreases from Multinomial to Gaussian and Bernoulli (yellow column). The difference in accuracy that occurs when analyzing these three algorithms is the result of several factors, such as the construction of the algorithm or the type of distribution on which it is based. The latter factor is easy to observe and analyze. According to the typology of the application and the field of applicability, the Multinomial distribution was the right choice for classifying tweets into categories (political orientations). For our data set, the Gaussian distribution and the Bernoulli distribution generated satisfactory performances, but below the performance of the Multinomial distribution. To analyze the accuracy, we use the Calibrated (sigmoid and isotonic) Naive Bayes algorithm and verify the accuracy of the prediction in the three cases (uncalibrated, isotonic calibrated, sigmoid calibrated). Calibration can improve prediction accuracy (Figure 11). For the data set used in our research, isotonic calibration provides the best accuracy, around 80%, which is similar to the Multinomial classifier. Based on the validation of the accuracy of the three algorithms, as well as of the calibrated variants, we concluded that the models can be used for the proposed purpose, namely, to identify the political orientation in tweets.