## 2 Related Work
### Arabic Dataset and Opinion Mining Methods  
[PERSON] et al. [16] provided information on studies that contribute to overcoming the lack of Arabic datasets related to opinion mining. Arabic opinion mining datasets extracted from the Twitter platform are the majority, followed by those extracted from reviews on websites and through applications. Accordingly, most Arabic opinion mining studies use available social media data with no more than three labels: positive, negative, and neutral, unlike the review data [14]. One study that overcame the lack of Arabic datasets is [PERSON] et al. 's [17]. The authors in [17] presented the Hotel Arabic Reviews Dataset (HARD), the Arabic dataset's most extensive public hotel reviews for machine learning applications and subjective opinion mining. It consists of 409,562 unbalanced reviews about hotels collected from the Booking.com website. The balanced subset of the dataset is over a hundred thousand reviews. Each review has a rating on a scale of one to five stars. The review text is in modern standard and colloquial Arabic and includes emojis and emoticons. Emoji is the typical expression of emotion and concepts through digital Unicode graphic icons. This form of representation is the latest generation of emoticons that are most popular in social media through mobile applications [18]. The authors in [17] have published baseline results for opinion mining using the HARD dataset. They used lexicon-based methods and different machine learning methods. They achieved an accuracy of 76.1% as the highest testing result for rating classification using the logistic regression (LR) algorithm with term frequency-inverse document frequency (TF-IDF). This combination was used unigram and bigram on the unbalanced HARD dataset. TF-IDF is a mathematical measure that explicates a word's importance in a document in a series of documents [19]. The authors removed the neutral reviews with a rating of three from the balanced HARD dataset before the experiment. Thus, the balanced HARD dataset has four rating levels, while the unbalanced version has five. In [20], the authors developed different deep and machine learning models for opinion mining in the reviews' domain. They used a subset of the HARD dataset, comprising 62,500 random reviews, to predict the five rating levels. The highest achieved accuracy for five labels of rating classification on the HARD dataset is 74.2%, while precision, recall, and f1-score results for each are 74.0% when using the random forest (RF) model. The following best models are based on convolutional neural networks (CNNs), decision trees (DTs), gated recurrent units (GRUs), and bi-directional recurrent neural networks (BiLSTMs). They achieved the following accuracy results, respectively: 67.4%, 66.4%, 65.1%, and 63.1%. Different studies created baselines of opinion and emotion detection in other languages. For instance, [PERSON] et al. [21] proposed a deep neural network-based emotion detection model from the textual data for the Urdu language to classify emotions of their presented UNED corpus. [PERSON] et al. [22] proposed a latent emotion memory network model that learns the latent emotion distribution in the data without external knowledge. The model receives an emotional bag-of-words as input, removes only stop-word tokens, and keeps the words presented in English or Chinese lexicons. Aspect-based sentiment analysis is an active area of research, especially concerning review data. However, observations have shown that aspect-based sentiment analysis models trained on a dataset from one domain do not generalize well to another dataset belonging to another domain [23]. In [24], the authors enhanced the aspect-based sentiment analysis robustness by improving the model, data, and training. They tested data from different domains. Their methods can be applied to other artificial intelligence-based tasks.