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

number of features. Then, it’ll use hyper-plane to differentiate between features and classes of emotion. d. NB NB includes several algorithms of classification based on the Bayes Theorem. The NB classifier presents significant results when it is used for text analyzing data. Such an algorithm offers a prospect examining the study’s dataset [12]. e. Multinomial (NB) Multinomial NB classifier works on the concept of term frequency, which suggests what percentage times the word occurs during an extremely document. MNB is specially designed for text data and a particular version of Naive Bayes [6]. MNB tells two facts about whether the word appears during a very document and its frequency there in document. III. L iterature R eview Although there are many studies during this domain, one amongst the tough challenges for all researchers during this domain is to use emotion analysis and classification for Arabic tweets, which remains limited, most Arabic studies specialise in sentiment analysis to classify tweets into positive/negative classes, underestimating the utilization of emotion detection and analysis to draw down different emotions. The literature review presents the foremost recent works on emotion detection in languages. Mansy, A et al.[1] researchers proposed an ensemble deep learning approach to research Emotion from user text in Arabic Tweets. They evaluated using the SemEval-2018-Task1- dataset published in a very multilabel classification task. The proposed model was supported three deep learning models. Two models are particular styles of Recurrent Neural Networks (RNNs), the Bidirectional Gated Recurrent Unit (Bi-GRU) and Bidirectional Long Short Term Memory Model (Bi-LSTM). The third may be a pretrained model (PLM) supported Bidirectional Encoder Representations from Transformers (BERT) NAMED MARBERT. The results of the proposed ensemble model showed outperformance over the individual models (Bi- LSTM, Bi-GRU, and MARBERT). They showed an accuracy of 0.54, precision of 0.63, 0.55 in an exceedingly recall, 0.70 in Macro F1 Score, and 0.52 in micro F1 Score. In addition, Khalil et al. [2] proposed a Bi-LSTM deep learning model for EC in tweets written in Arabic that were employed in the SemEval-2018 dataset. The Aravec with CBOW for the word embedding phase has been employed in feature extraction. Their results have shown an Accuracy of 0.498, and a Micro F1 score of 0.615. Another study on Arabic emotion analysis was proposed in [3]. The authors addressed the emotion detection problem in Arabic tweets. A tweet may have multiple emotional states (for example, joy, love, and optimism). during this case, the emotional classification of tweets is framed as a multilabel classification problem. The proposed approach combined the transformer-based Arabic (AraBERT) model and an attention- based LSTM-BiLSTM deep model. The approach used a publicly available benchmark dataset of SemEval-2018 Task 1, where the dataset is formed for multilabel detection of emotion in these tweets. The findings show that such an approach presents accuracy of nearly 54. A multilabel classification was employed to detect emotions in Arabic tweets by [4]. The authors proposed three models: the Deep Feature-based (DF), the Human engineered feature- based (HEF) model, and the Hybrid model of both models (HEF and DF). They assessed the execution of the proposed model on the SemEval-2018, IAEDS, and AETD datasets. For feature extraction, they used Hourglass of emotions, frequency-inverse document, Lexical sentiment features, and Lexical emotion features. the most effective performance results for the hybrid model were achieved when combining the TF-IDF of unigrams, TF-IDF of the Part of Speech (POS) tags, HGE, LSF, and LEF with the DF model. The findings report that the hybrid model exceeded the HEF and DF models in the datasets. The hybrid achieved for every IAEDS, AETD, and SemEval- 2018 datasets an accuracy of 87.2, 0.718, and 0.512, respectively. 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 13 ( )G Year 2023