Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1
Fig. 1: Summary of the emotion detection in Text Furthermore, deep learning detects emotions. For instance, AlZoubi et al. [5] have implemented an ensemble approach that contains Conventional Neural Networks (CNN), Bidirectional GRU-CNN (BiGRU-CNN), and XGBoost regressor (XGB) to be utilized in solving the EC of the SemEval-2018 dataset written within the Semitic. The ensemble approach used TF-IDF, word- level embedding, and lexicon features. Results show that their model achieved a precision of 69. In addition, Hussein, et al. [6] followed the machine learning method to detect emotion in Text Mining Data supported Arabic Text. They collected text mining data from the internet while focusing on four emotion classes (sad, happy, afraid, and angry). Three sorts of techniques are used supported machine learning approaches include KNN, NB, and SVM algorithm. The findings also showed that NB had the best accomplishment regarding accuracy. NB classifiers achieved 70, comparing to SVM that obtained 68.33, while KNN yielded 51.67. Saad et al. [7] proposed a similar model to categorize emotions from the Malay language. The dataset used consists of Malay children’s short stories. over 200 short stories were collected, each story varying from 20-50 words. The TF- IDF is extracted from the text and classified using SVM and DT. Four common emotions, happy, angry, fearful, and sad, are classified using the 2 classifiers. Results showed that the choice Tree outperformed the SVM by a 22.2 accuracy rate. Fig 1 summarizes the acceptable emotion methods reviewed during this paper and sorted on the most recent date. Fig. 2: Shows the main phases of the methodology 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 14 ( ) Year 2023 G
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