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

performance of chatbots and other automatic feedback frameworks[4]. In Additionally, text emotion analysis has been a promising research topic. Analyzing the texts and identifying emotion from the words and semantics could be a difficult challenge. The paper aims to automatic recognition of emotions in texts written in the Arabic language by employing a model for Emotion Classification (EC) into emotion classes: Sadness, joy, fear, and anger with the algorithms of machine learning. This approach utilized the SemEval- 2018 Task1 reference dataset and focused on four emotion classes (Joy, Sad, Angry, and fear). Five forms of algorithms are used supported the machine learning approach, namely K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Multinomial (NB). KNN, DT, SVM, NB, and Multinomial NB classifiers are utilized in the classification process since they offer the foremost satisfactory and better accuracy results among all other classifiers. The findings showed that the choice Tree and K-Nearest Neighbor classifiers have the best accomplishment regarding accuracy, 0.74, While the NB and Multinomial NB classifiers acquired 0.69, and also the SVM obtained 0.63. The structure of this study continues to section II, which presents Problem Definition and Algorithm while section III offers the recent related work on Arabic emotion recognition. Section IV describes the Methodology and results of emotion analysis from Arabic texts and discusses the results. Section V provides the conclusion and future work. II. P roblem D efinition and A lgorithm This section briefly presents the Problem Definition and Algorithm for the detection of emotion in texts written in the Arabic language. a) Problem Definition Most research papers in this field focus on negative or positive emotion analysis and do not go deeply into emotion analysis, especially in Arabic. Research in emotion analysis for Arabic has been minimal compared to other languages like English. This paper addresses the emotion detection problem in Arabic tweets and presents a model to categorize emotions into sadness, joy, anger, and fear. Further- more, the current work can provide many benefits for governments, health authorities, and decision-makers to monitor people’s emotions on social media content. Additionally, it can improve business strategies according to customers’ emotions and recognize potential criminals when analyzing people’s emotions after an attack or crime. b) Algorithm Definition The Machine Learning approach learns from the info and tries to hunt out the relation between a given input text and also the corresponding output emotion by building a prediction model. This approach is split into two categories: i. Supervised learning approach Based on a labeled or annotated dataset, the supervised approach takes a component of this data for the training process using an emotion classifier. This trained data is then examined, and a model is made. The remaining data within the dataset is classed supported this previously trained classifier into the emotion category. ii. Unsupervised learning approach The unsupervised approach relies on a non- labeled dataset. The approach inherent the drawbacks of the ML algorithm. It requires an oversized dataset for the training process to be accurate. The Machine Learning approach solves the emotion detection issue by categorizing texts into various emotion classifications using the mentioned algorithms. This process is usually done employing a supervised or unsupervised ML technique. To categorize the tweet into each categorization (anger, joy, sadness, and fear), we applied five different supervised machine-learning approaches: KNN, DT, SVM, NB, and NB. Following could even be an inventory of the classifiers discussed during this work: a. K-Nearest Neighbor (KNN) KNN is addition- ally a fairly AI supported machine learning algorithms in classification, processing, statistical pattern recognition, and much of more. This method in our experiment can classify an emotion correctly[15]. KNN classifies a replacement instance within the test set supported the shortest distance between it and numeric neighbors (k) stored within the training set using the Euclidian Distance equation[13]. b. Decision Tree (DT) A Decision Tree could even be a mode of a tree structure utilized in classification and regression models. It breaks down the datasets into smaller subsets and incrementally develops them into nodes and leaves. The branches of the selection tree represent the category of the datasets. the selection tree is split into four emotion classes: joy, sadness, anger, and fear. The selection tree’s goal is to substantiate it achieves maximum separation among classes at each level. c. Support Vector Machine (SVM) SVM could even be a supervised ML algorithm. The model is straightforward, and far of individuals value more highly to use this model thanks to its less computational power and it gives significant accuracy. SVM conducts linear classification and performs non- linear classification alright [6]. This model’s idea is straightforward: The algorithm plots each data item as some extent in n-dimensional space representing the 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 12 ( ) Year 2023 G