# _algorithms_
## 2 Related Work  
Sentiment analysis is a fundamental NLP task and admits a wide range of real-life applications. Shallow machine learning models, such as Support Vector Machine (SVM), Logistic Regression, Decision Trees and Naive Bayes [9], were widely explored in early research. With the rise of deep learning technologies, sentence expression models, such as RNN, CNN [10] and their variant versions, were proposed [11; 12; 13; 14; 15]. In addition, in order to better integrate or extract more features, attention mechanisms [16] and models [17] based on Transformer also emerged. With the accelerated evolution of the internet, more and more users tend to share their experiences and opinions on review sites. The content of review sites cover movies, hotels, and products to events. However, not all reviews are genuine and credible, as many comments are spam, written to gain profits or fame or to undermine the reputation of a product with fake reviews. These spam reviews might mislead consumers about the target product and might negatively impact user experiences. Spam review detection denotes a new NLP field that aims to combat this phenomenon. Spam review detection is mainly based on compound features of words, emotion and user behavior. Before the pre-training model was proposed, people tended to use N-Gram features to represent the meaning of comment text. For example, [PERSON] et al. [18] proposed a method using character n-grams as features to capture lexical content, as well as stylistic information, in spam opinion. [PERSON] and [PERSON] [19] proposed using character-level n-gram features in reviews and support vector machines as classifiers. [PERSON] et al. [20] combined text mining and semantic analysis to design deceptive review detection methods, which was shown to yield good performance in practical applications. [PERSON] et al. [21] utilized psychological analysis and computational linguistic features to analyze spam comments, and proposed three detection methods, which achieved promising experimental results. [PERSON] et al. [22] devised multiple attention units and fusion attention units to capture the relationship between the reviewer and the product in order to detect spam comments. [PERSON] et al. [23]firstly detected spammers with graph neural network, then employed a Markov random field to correct the classification results and improve accuracy. Although the above-mentioned approaches are extensively utilized, they have their shortcomings. The aforementioned machine learning models use manually engineered features as inputs, which may have poor performance for complex problems. On the other hand, despite the fact that significant advances have been achieved in deep learning, their black-box nature makes them less trustworthy. Actually, TM has achieved excellent results in many NLP works. [PERSON] [24] resorted to TM to convert the input of aspect-based sentiment analysis into a binary form of limited information, introduced additional knowledge from semiwordnet, and built an interpretative model with TM. [PERSON] et al. [25] used TM's conjunction clauses to learn and represent known classes and established a novelty scoring mechanism. In a subsequent work, [PERSON] et al. [26] adopted TM clauses to capture the vocabulary and semantic attributes of true or false news to predict news authenticity, and to calculate the credibility of fake news. [PERSON] et al. [27] applied TM on the IMDb data set as well as on a medical data set. They converted complex text content into simple propositional formulae with good interpretability. [PERSON] et al. [28] resorted to the TM model to mine the relationship between emotions and semantics. In this article, we used TM to perform Chinese NLP tasks, including sentiment analysis and spam review detection. We did not only extract comment word features, but also we used additional features to represent user behavior and product attributes.