## 3 Approaches
### Semi-Supervised  
In this first subsection, the spam online reviews based on the semi-supervised method are discussed. A semi-supervised approach or learning is a technique that operates on a dataset that combines labeled and unlabeled instances [131]. That is to say, the semi-supervised method is a merge between unsupervised learning (no labeled instances) and supervised learning (labeled instances). Usually, the amount of unlabeled parts of sets exceeds the number of labeled sets by far. In literature, this method obtained attention between researchers with its performance to handle different types of datasets. For example, Ref. [132] argues that there are many works presented to solve the identification between real and spam reviews. However, the issue is still challenging to manage for supervised learning due to the lack of labeled data samples as well as imbalanced problems. Therefore, the authors see that using a semi-supervised learning approach is better for spam reviews problem. Their approach-Ramp One-Class SVM- consists of a nonconvex semi-supervised method. Which operates on one-class data in order to deal with the lack of labeled datasets. Also, solving the outliers and non-review using the nonconvex properties of the proposed approach loss function. The experiments were executed on two datasets, namely Yelp and Ott. The outcomes show that the proposed method outperforms other techniques in terms of accuracy, precision, and recall. Moreover, the work in [133] also studied the performance of the semi-supervised method on spam reviews detection. The hybrid semi-supervised learning approach that they presented relies on the user-product and users' characteristics relations. In their work, the hybrid PU-learning-based spammer detection (hPSD) starts its detection process by producing various positive samples. Then the semi-supervised learning classifiers are evaluated on the movie dataset. The hPSD achieves the best results when compared with other approaches. Furthermore, the approach used the real-life Amazon spam reviews detection dataset for more examinations. Another recent framework proposed by [134] for spam reviews detection using semi-supervised learning. The work simply used the adversarial training mechanism that exploits the Generative Pre-Training 2 (GPT-2) abilities to detect spam reviews against unlabeled and labeled data. The experiments were performed on the TripAdvisor and YelpZip datasets. The presented model obtained the highest results with 7% more in terms of accuracy. Moreover, due to the lack of labeled data, the model generates synthetic samples (spam/non-spam reviews) to prove more labeled instances to learn better. [PERSON] et al. [135] stated that in real-world situations the datasets that are used usually lack the required labels to perform on supervised models. Therefore, applying the semi-supervised learning approaches can be more efficient. The purpose of this study is to explore the effectiveness of the various type of semi-supervised learning as a classification method. The authors examined four different semi-supervised models in order to classify the spam online reviews for hotels. Additionally, the experiments comparison shows that the Naive Bayes acquire the best results with 93% accuracy. The study demonstrates the need to mitigate the labeling efforts while focusing more on model performance when the labels are limited. Whereas, [PERSON]. [136] introduced a dictionary based on online reviews and social network terms to find the hidden pattern and relationship of these terms. A number of language features were used, including, length of reviews, presence, and frequency of bigram, and presence and frequency of unigram. The work employed both supervised and semi-supervised methods to detect spam reviews with the help of linguistic and behavioral features. According to previously addressed studies, the semi-supervised approach is desired the most when the data are not fully labeled. Such scenarios can usually occur in real-world situations.