Fake Review Identification Methods Based on Multidimensional Feature Engineering

Abstract

Product reviews in electronic platforms are very valuable to potential customers, product manufacturers, and product sellers. Their data contain huge business opportunities. Therefore, this paper analyzes the views, attitudes, and emotions expressed in these reviews. It presents three fake review identification methods based on multidimensional feature engineering. Under the premise of adding product feature extraction and opinion sentence judgment, six feature parameters are defined to identify fake reviews, and a fake review identification model based on multidimensional feature engineering is constructed. Then, the effectiveness of the selected feature engineering is verified. Based on the multidimensional feature engineering model, a fake review identification algorithm based on multidimensional feature engineering of union relationship, an identification algorithm based on weighted multidimensional feature engineering scoring, and an identification algorithm based on weighted multidimensional feature engineering classification are proposed. The execution effects of the three methods are compared. Fake review identification models based on multidimensional feature engineering can effectively filter fake reviews.

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

“…In engineering applications, it is usually necessary to install multiple sensors at the key cross-section of the system to collect multi-channel information and extract statistical features from the vibration signals collected from each channel, but the increase in the number of features will undoubtedly produce the problem of "dimensional catastrophe" [27,19,15,14,10]. crucial for developing machine intelligence fault diagnosis and decision-making techniques for industrial big data [23,2,20].…”