Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1

Fig. 6: The percentage of each class in the dataset Fig. 7: Evaluation Metrics V. E valuation M etrics We use precision, accuracy recall, and F- score accuracy, in this study, to measure the EC’s performance. Precision, also named positive predictive value, is the documents’ number labeled correctly as belonging to the positive class. Sensitivity, or recall, is the documents’ number that is not labeled as belonging to the positive class. Another measurement that combines recall and precision is F-score. The F-score indicates how accurate the classifier is (how many instances are correctly classified) and its robustness (it does not miss many instances). The last measure is accuracy, which indicates the suitability of a given classifier[13]. Calculations of the measurements are given in Fig. 7. VI. R esults and D iscussion This section reports the performance results and discusses the model. We trained the model on the training dataset for the SemEval- 2018 dataset, and reported on the model performance on the test dataset. The Decision Tree and K- Nearest Neighbor classifier’s accuracy was 0.74, the NB and Multinomial NB classifiers obtained 0.69, and the SVM 0.63. The results could be more encouraging regarding accuracy. Fig. 8 displays the accuracy comparison of various machine learning models. Fig. 8: Results of the accuracy using different Classifiers Emotion Detection in Arabic Text using Machine Learning Methods © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 17 ( )G Year 2023