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

Fig. 3: Snapshot of the training dataset before preprocessing Fig. 4: Snapshot of the testing dataset before preprocessing IV. E xperimental E valuation This section discusses the used dataset and the method- ologies to identify emotions of tweets written in Arabic by utilizing (5) algorithms of machine learning: KNN, DT, SVM, NB, and Multinomial NB. The final section highlights the outcomes of this process. The main phases of the methodology are shown in Fig. 2. The methodology consisted of the dataset, preprocessing, features engineering, supervised machine learning, and classified emotions based on four emotions (anger, joy, sadness, and fear). a) Dataset This section discusses the used dataset in which the experiments are performed using the reference emotion detection SemEval-2018 (Affect in Tweets) dataset. The dataset is the public benchmark dataset created for the detection of emotions in tweets written in Arabic. Each tweet is labeled as one of the emotions ( joy, anger, sadness, and fear. ). All these tweets are in Arabic text. We used only the EIoc for our experiment with four basic emotion categories. The training dataset trains the classifier and the test dataset examines the structured model to show identify the value of trained model. Figure (3) highlights the training dataset and Figure (4) highlights testing dataset. b) Preprocessing Data preprocessing is taken into account one among the essential phases in machine learning to avoid misleading results and obtain better insights. during this section, the preprocessing steps are discussed as follow: The tweet from the SemEval2018 dataset has been preprocessed using the foremost common preprocessing techniques, like removing stop words, repeating chars, English characters, mentions, punctuation marks, and Arabic diacritics. Also, text nor- malization has been added. 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 15 ( )G Year 2023