Sentiment analysis involves analyzing people’s responses, emotions, and opinions in the form of text toward an object, such as a book, service, or video. This type of analysis can be utilized on YouTube comments using machine learning algorithms and techniques to classify them as positive or negative. Classifying a YouTube video based on its comments can save viewers’ time and assist YouTube content creators in gauging the impression of their viewers, leading to improvement in content quality in general on the platform.

The language that holds the fourth spot in terms of popularity on the internet is Arabic ^[1]. It takes an essential place within the realm of sentiment analysis and needs more research about it. There are several characteristics of the Arabic language which make it unique compared to other languages. First of all, it has 28 letters with no capitalization format. In addition, words can be written by connecting Arabic letters. For instance, writing a letter alone will be different from writing it in a middle of a word ^[2]. Secondly, the Arabic language can be written differently based on its dialects. On the internet, specifically social media, it is common to see the language written using various dialects.

Natural language processing is a part of Artificial Intelligence, and it is a technique used to analyze and process human language by enabling the computer to interpret the intent and sentiment of the writer or the speaker. In addition, it allows the computer to understand the whole meaning of the sentence. The popularity of utilizing Natural Language Processing (NLP) has recently increased noticeably. However, the number of its applications in the Arabic language is incomparable with the English Language. It is clear that English research papers outnumber Arabic ones. When it comes to Arabic YouTube comments, we can capitalize on them to save the time of viewers to know whether it is worth it to watch a specific video or not.

2. Arabic Sentiment Analysis

Numerous languages, including English, have investigated sentiment analysis thoroughly in [3,4,5,6,7,8,9]^{[3][4][5][6][7][8][9]}. Despite Arabic being the fourth most used language online, there has not been much research on the usage of sentiment analysis in it ^[1]. Arabic sentiment analysis is a challenging task due to a variety of challenges with the language. In Arabic, the same word might have a variety of meanings depending on the context. Arabic also has a rich morphology, with verb forms that are difficult to understand and elaborate syntactic patterns. The wide range of dialects spoken in Arabic is a significant barrier to sentiment analysis. In the region of the Middle East and North Africa, Arabic is spoken in a number of dialects, with substantial variations in vocabulary, syntax, and pronunciation. These factors make it challenging to develop accurate sentiment analysis models for Arabic texts. Despite the challenges, there have been successful research studies within the framework of sentiment analysis applied to the Arabic language.

3. Literature on Arabic Sentiment Analysis

Al-Tamimi et al. ^[10] suggested a classification model of Arabic YouTube comments that was done through the manual collection, annotation of comments, and the use of popular supervised classifiers. The classifiers tested during experiments are SVM-RBF, KNN, and Bernoulli NB classifiers. They demonstrated that a normalized dataset that has two classes (positive and negative), along with the use of SVM-RBF, excelled in comparison to other classification approaches, yielding an f-measure of 88.8%. Alakrot et al. ^[11] presented the findings from predictive modeling for identifying antisocial behavior in Arabic online communication, such as identifying comments that contain offensive or derogatory language. They built a dataset of 15,050 Arabic YouTube comments that was ready for prediction purposes. Three annotators from three distinct Arab nations carried out the labeling process. The reason for selecting different Arab nationalities of annotators is to ensure that dialectal comments can be understood by most Arabs. The comment is labeled offensive if at least two of the annotators agree that they see it offensive. In their experiments, they used the Support Vector Machine classifier for training the dataset. They applied pre-processing techniques and attributes that enable the classifier’s training process that is more accurate—with 90.05% accuracy—than classifiers used in other previous works on Arabic sentiment analysis. Another study was implemented by Mohaouchane et al. ^[12] to detect the offensive language in Arabic YouTube comments using deep learning. This research employed a dataset that was made publicly available and had been gathered and labeled by Alakrot et al. ^[11]. Mohaouchane et al. ^[12] presented the findings from a comparison of the performance of four various neural network designs. Using a classified dataset of Arabic YouTube comments, these networks were trained and put to the test. The comments were represented by Arabic word embeddings after this dataset had undergone a number of pre-processing steps. They additionally adjusted the hyperparameters of the neural network models using Bayesian optimization approaches. With the use of 5-fold cross-validation, they trained and evaluated each network. The highest Recall was attained with the CNN-LSTM, which was 83.46%. However, CNN attained the highest accuracy, precision, and F1-Score with 87.84%, 86.10%, and 84.05%, respectively. Mohammed and Kora ^[13] conducted a study that made two contributions: they started by presenting a corpus of 40,000 Arabic tweets that had been labeled and covered a range of subjects. Then, for Arabic sentiment analysis, they offered three deep learning models: CNN, LSTM, and RCNN. They verified the performance of the three models on the corpus using word embedding. Their test findings showed that LSTM performed better than CNN and RCNN, indicating an average accuracy score of 81.31% and 88.71% when the corpus was augmented using data augmentation techniques. In ^[14], a pioneering deep-learning model for evaluating sentiment in the Arabic language was presented. To extract local features, the model employs a single layer of CNN architecture while maintaining long-term dependencies. It employs two layers of LSTM. The SVM classifier is used to create the final classification from the feature maps acquired from the CNN and LSTM. Additionally, to support this model, they have used the FastText words embedding model. A training set of 15,100 reviews and a test of 4000 reviews were used with the model. The results show that this model performs outstandingly in terms of classification, with 90.75% accuracy. In their study, Hadwan et al. ^[15] used sentiment analysis and machine learning techniques to analyze Saudi Arabian citizens’ reviews on Google Play and the app store. They conducted their study using a brand-new dataset made up of 8000 user reviews in Arabic that were acquired from social media, Google Play, and the app store. The dataset was subjected to several techniques, and the findings indicate that the k-nearest-neighborhood (KNN) method produced the highest accuracy with 78.46% compared to other employed techniques. Khabour, Al-Radaideh, and Mustafa ^[16] proposed a semantic orientation approach to calculate the overall polarity of Arabic subjective texts. Their methodology involves utilizing a domain ontology and sentiment lexicon to extract and weigh semantic domain features based on ontology levels and dataset frequencies. The authors evaluated their approach using an Arabic dataset from the hotels’ domain, which consists of a total of 15,572 reviews annotated with positive, negative, and neutral classes. The experimental results indicated an overall accuracy of 79.20% and an f-measure of 78.75%. In their research, Arwa Alqarni and Atta Rahman aimed to analyze the sentiment of Arabic tweets with the consideration of the COVID-19 pandemic in Saudi Arabia. They gathered data across two consecutive time periods from the main Saudi Arabian cities of Riyadh, Dammam, and Jeddah. The tweets were pre-processed and annotated with positive, negative, or neutral sentiments. The total number of those pre-processed tweets was 90,187. For sentiment classification, the authors employed deep learning algorithms, namely Convolutional Neural Networks (CNN) and Bi-directional Long Short-Term Memory (BiLSTM). The experimental results demonstrated that the CNN model achieved an accuracy of 92.80%, while the BiLSTM model achieved 91.99% accuracy. The key points from the papers reviewed in this section are extracted and presented in Table 1.