Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1
© 2023. Fatimah Khalil Aljwari. This research/review article is distributed under the terms of the NoDerivatives 4.0 International (CC BY-NC-ND 4.0). You must give appropriate credit to authors and reference this article if parts of the article are reproduced in any manner. Applicable licensing terms are at https://creativecomm nd/4.0/. Global Journal of Computer Science and Technology Interdisciplinary Volume 23 Issue 1 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 0975-4172 Abstract- Emotions are essential to any or all languages and are notoriously challenging to grasp. While numerous studies discussing the recognition of emotion in English, Arabic emotion reco remains in its early stages. The textual data with embedded emotions has increased considerably with the Internet and social networking platforms. This study aims to tackle the challenging problem of emotion detection in Arabic text. Recent studies found that dialect diversity and morpho Arabic language, with the limited access of annotated training datasets for Arabic emotions, pose the foremost significant challenges to Arabic emotion detection. Social media is kind of communication where users can share their thoughts and express emotions like joy, sadness, anger, surprise, hate, fear, so on some range of subjects in ways they’d not typically neutralize person. Social media also present different challenges which include spelling mistakes, new slang, and incorrect use of grammar. The previous few years have seen a giant increase in interest in text emotion detection The study of Arabic emotions might be a results of the Arab world’s conside politics and thus the economy. There are numerous uses for the automated recognition of emotions within the textual content on Facebook and Twitter, including company development, program design, content generation, and emergency response. in line with recent studies, it’s possible to identify emotions in English-language information. Keywords: emotion detection, machine learning, arabic text, KNN, DT, SVM, naive bayes GJCST-G Classification: FOR Code: 170203 EmotionDetectioninArabicTextusingMachineLearning Attribution Year 2023 & Print ISSN: 0975-4350 - logical complexity in the becoming a more popular rable influence on global Methods Strictly as per the compliance and regulations of: -NonCommercial- ons.org/licenses/by-nc- : G gnition research . . Emotion Detection in Arabic Text using Machine Learning Methods By Fatimah Khalil Aljwari University of Jeddah
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