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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.

The paper describes the process of mining opinions from Chinese reviews of products sold online. The structure of Chinese reviews is free, which leads to a more complicated relationship between opinions and features. The paper introduces two main steps of opinion mining: feature extraction and opinion direction identification. The feature extraction function first extracts "hot" features that a lot of people have expressed their opinions in their reviews, and then finds those infrequent ones. In order to improve the accuracy of the experiment, redundant features are removed. The opinion direction identification function takes the generated features and summarizes the opinions into two categories: positive and negative. We extract adjectives and negative adverbs as opinion words and use the Naïve Bayes classifier to identify their direction. By direction, we mean whether an opinion is positive or negative.