# Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
## 2 Related Works
### Machine- and Deep-Learning Methods  
Currently, two main methods are used to detect deceptive reviews: conventional machine-learning and deep-learning classification methods. Among the standard approaches to machine learning, supervised learning is the most commonly adopted method. However, because of the difficulties in obtaining marked datasets and the low accuracy of human classification, much of the literature has either adopted simulated datasets or small-size real-world datasets for model training. [PERSON] et al. [17] pioneered the use of machine learning for deceptive-review detection. Meanwhile, real reviews of 20 of the most popular five-star hotels in the Chicago area, which were extracted from the website TripAdvisor(including 400 real positive and 400 real negative reviews), were collected. Next, Amazon Mechanical Turk (MTurk) was employed to collect 400 false positive and 400 false negative reviews of the same hotels. Thus, 1600 real and deceptive reviews were compiled. This dataset was subsequently referred to as the \"gold-standard\" dataset thereafter. Three approaches were used for feature extraction, namely, \(n\)-grams, psychological features, and a part-of-speech feature, and a support vector machine (SVM) classifier was trained to find a higher dimensional hyperplane between the two categories of data. The accuracy rate of the constructed model was found to be 89.8%, which was almost 30% higher than that of human judgment (61.9%). However, [PERSON] et al. [18] found that the linguistic features of deceptive and real reviews in MTurk were substantially different, whereas those in Yelp tended to be similar. Hence, the deceptive reviews in Yelp were significantly more difficult to identify. The Unigram feature sets and SVM classifiers yielded an accuracy rate of 88.4% when used to detect the deceptive reviews in MTurk [17], whereas in Yelp, the accuracy rate dropped to 67.8% [18]. These findings demonstrated that identifying deceptive reviews in the real world is more difficult than in marketplaces. Thus, the use of closed marketplace data does not reflect real-world review contexts because the length, quantity, content, and repetition of the reviews in MTurk were highly correlated to the cost of services instead of the experience of reviewers. The majority of the past studies on deceptive-review detection using machine-learning technology tended to use conventional word forms, shallow syntactic features, and deep linguistic features as text features. Under such conditions, machine-learning models rely on prior knowledge where the accuracy is limited by the selected feature sets. Furthermore, because the datasets used in the studies are not comparable, determining the optimal combination of feature sets is not possible. In addition, applying the same feature-selection method and classification model to different datasets may yield different accuracy rates. The semantics of artificially simulated deceptive reviews and the deceptive reviews generated in marketplaces are not the same as those written by humans for real-world contexts. Therefore, the expected classification performance of such models on real-world datasets could not be obtained. Nevertheless, [PERSON] et al. [19] found that conventional machine learning has suffered a bottleneck where, even if more training data were available, the accuracy could not be noticeably improved. For this reason, scholars have instead shifted toward deep-learning methods to identify deceptive reviews. The majority of Taiwanese studies on deep-learning deceptive-review detection models have tended to focus on the Samsung case. According to a set of deceptive reviews extracted from TaiwanSamsungleaks.org, [PERSON] et al. [20] conducted six experiments, namely, temporal analysis, sentiment analysis, semantic analysis, social-network analysis, machine learning, and deep learning. The deep-learning method utilized text features to build a dictionary, which was then converted into strings and ensured that each review had the same length.