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

Emotion Detection in Arabic Text using Machine Learning Methods Fatimah Khalil Aljwari 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 recognition research 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- logical complexity in the 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 becoming a more popular 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 considerable influence on global 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. ”However, we are tuned in to only some initiatives to include Arabic content. Hence, we shall develop a machine- learning model for emotion detection from Arabic textual data on social platforms. This study categorizes the texts supported emotions, anger, joy, sadness, and fear, using supervised machine learning approaches. We used five different machine learning algorithms, namely Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NB), Multinomial Naive Bayes (NB), and Support Vector Machine (SVM) to classify emotions in Arabic tweets. These algorithms assessed our proposed approach on the dataset of Arabic tweets provided by SemEval-2018 for EI-oc. This meant that the results of the machine learning approaches were admissible. These results found that the selection Tree and K- Nearest Neighbor classifiers have the simplest accomplishment regarding accuracy, 0.74, While the NB and Multinomial NB classifiers acquired 0.69, and also the SVM obtained 0.63. Keywords: emotion detection, machine learning, arabic text, KNN, DT, SVM, naive bayes. Author: Computer Science and Artificial Intelligence Department University of Jeddah, Jeddah, Saudia Arabia. e-mail: fatima6794o5@gmail.com I. I ntroduction urrently, social media plays a necessary role in way of life and practice. immeasurable individuals use social media for various purposes. Every second, a major amount of knowledge flow via online networks, containing valuable information that may be extracted if the information are correctly processed and analyzed[8]. Social networking media became essential for expressing emotions to the planet due to the fast growth of the web. Several individuals use textual content, audio, video, and images to express their emotions or perceptions[9]. The Affective Computing research field has been an energetic research domain and recently gained great popularity. It aims at providing machines with a human-like ability to grasp and answer human emotions, with more natural interaction between humans and machines[10]. Emotions are a vital component of human life. Emotions affect human decision- making and can enable us to speak with the planet in a very better way. Emotion detection, also called emotion recognition, identifies an individual’s feelings or emotions, for instance, joy, sadness, or fury [9]. ”Emotion detection,” ”emotion analysis,” and ”emotion identification” are all expressions that are periodically used interchangeably. The sentiment analysis could be a means of evaluating if data is negative, positive, or neutral. In contrast, emotion recognition specifies different human emotion types, like joy, love, sadness, happiness, anger, and surprise[9]. quite 400 million people speak Arabic, the official language of twenty-two countries. It is the Internet’s fourth most generally used language [10]. Languages utilized in social media, such as Twitter, differ wildly from that utilized on other platforms, like Wikipedia. The English language has been highly determined within the emotion detection field, including datasets and dictionary availability, in contrast to the Arabic, which has minimal resources[11]. Emotion analysis has different applications in every aspect of our existence, including making efficient e-learning frameworks in step with the emotion of scholars, enhancing human- computer interactions, observing the mental state of people, enhancing business strategies supported customer emotions, analyzing public feelings on any national, international, or the political event, identifying potential criminals by analyzing the emotions of individuals after an attack or crime, and improving the C © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 11 ( )G Year 2023