Identification of Usefulness for Online Reviews Based on Grounded Theory and Multilayer Perceptron Neural Network

• Published in: Applied sciences Vol. 13; no. 9; p. 5321
• Main Authors: Hou, Jiani, Zhu, Aimin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 24.04.2023
• Subjects: Computer programs; Decision making; Electronic commerce; Grounded theory; Internet software; multilayer perceptron neural network; Neural networks; online review; Purchasing; the usefulness of online reviews
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13095321

In the context of the continuous development of e-commerce platforms and consumer shopping patterns, online reviews of goods are increasing. At the same time, its commercial value is self-evident, and many merchants and consumers manipulate online reviews for profit purposes. Therefore, a method based on Grounded theory and Multi-Layer Perceptron (MLP) neural network is proposed to identify the usefulness of online reviews. Firstly, the Grounded theory is used to collect and analyze the product purchasing experiences of 35 consumers, and the characteristics of the usefulness of online reviews in each stage of purchase decision-making are extracted. Secondly, the MLP neural network classifier is used to identify the usefulness of online reviews. Finally, relevant comments are captured as the subject and compared with the traditional classifier algorithm to verify the effectiveness of the proposed method. The experimental results show that the feature extraction method considering consumers’ purchase decisions can improve the classification effect to a certain extent and provide some guidance and suggestions for enterprises in the practice of operating online stores.