## 2 Literature Review
### Research on the Algorithm of Online Review Usefulness Recognition  
The identification of the usefulness of online reviews is fundamentally a binary problem, and some research have used machine learning methods to identify it. [PERSON] et al. classified the usefulness of online hotel reviews by the quality of reviews, the emotion of reviews, and the characteristics of reviewers and selected four classification models, DT, RF, PCA+LGR, and PCA+SVM, to conduct empirical research on different data sets. Through different analytical perspectives, it can be found that RF classification technology has the best predictive performance [18]. [PERSON] et al., different from previous studies, used a statistical regression model to analyze the influencing factors of the usefulness of online reviews and chose a support vector machine model to train attractive attribute classifiers [19]. Aiming at the problem that the existing opinion mining technology ignores the quality of comments, [PERSON] and [PERSON] proposed a method of first extracting representative comment features and then classifying the information quality of online comments using support vector machines [20]. Later, some scholars found that MLP neural networks performed better than traditional classifiers in text classification. For example, [PERSON] et al. and [PERSON] et al. used the multi-layer perceptron neural network to predict the usefulness of online reviews from the perspective of product information, review features, and text features and argued that the MLP neural network was better than the linear regression analysis method in the calculation of the minimum mean square error [21,22]. In conclusion, the core problem of this paper is to construct a classifier based on useful and useless samples of tags using supervised learning and then predict untagged comments as useful or useless comments. Based on the above literature review, a multi-layer perceptron neural network is selected as the classifier model, which identifies the usefulness of online reviews and compares them with other traditional classifiers to prove their superiority.  
To sum up, most of the existing studies on the usefulness of recognizing online comments only take the length of comments, the number of comments, the degree of emotion of comments, and other factors as identification characteristics based on the comment text itself. However, in the real situation, merchants' \"brushing\" behavior for the purpose of increasing sales volume or consumers' large product reviews driven by the interest of \"cash back on good reviews\" cannot be identified from the perspective of the identification characteristics above. Therefore, a feature construction method is proposed, considering the purchasing decision-making process of consumers. Based on the Grounded theory, the product purchasing experiences of 35 consumers are analyzed, and the features that can effectively identify the usefulness of online reviews are selected from the five stages of demand generation, information collection, product evaluation, purchase decision, and post-purchase behavior, respectively. Finally, the MLP neural network classification model is used to identify the usefulness of online reviews, and the optimal feature combination set is obtained by a multi-group comparison test, which is tested on other traditional classifiers. By crawling more than 6000 online comments from JD.com (accessed on 5 November 2022) as the subject data set, the feasibility and effectiveness of the proposed method are verified. The experimental results show that the feature construction method based on Grounded theory considering consumer decision-making processes proposed can effectively improve the classification effect of the MLP neural network. It provides theoretical support and technical guarantees for e-commerce business operations and consumer decision-making.