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

Fig. 12: The DT algorithm’s results Fig. 13: SVM algorithm’s results VII. C onclusion EC is a text categorization approach that aims to identify human feelings conveyed through texts. In recent years, Arab users have expressed their emotions on many of the issues raised through the Twitter platform. Therefore, this paper focused on the common classification algorithms such as DT, KNN, SVM, NB, and Multinomial (NB) and applied them to a tweet’s dataset as short text content. This study presented the approach for categorizing the emotions of tweets written in Arabic while utilizing the machine learning model. We used the dataset of Arabic tweets presented by SemEval-2018 for EI-oc task. This process used (4) emotion categories: Anger, joy, fear, and sadness. The approach achieved acceptable results with 0.74 for each of the KNN and DT, while the NB and Multinomial NB acquired 0.69; finally, the SVM achieved 0.63. Therefore, future research, including deep learning, is promising, primarily if provided with a large, good, annotated dataset. Also, future research on constructing and finding an Arabic dataset that is labeled correctly will aid and increase the advancements in textual emotion detection, because it will offer a dataset that can be utilized to compare various suggested investigations. VIII. F uture W ork The authors intend to examine this model on bigger datasets, assess the outcomes, and employ a hybrid approach that depends on deep learning and machine learning to classify emotions in texts written in Arabic and compare multiple methodologies. 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 19 ( )G Year 2023