Fake Review Identification Method Based on Topic Model and Att-BiLSTM

Abstract

The review rating system provides valuable information to potential users, but it also encourages the creation of profit-driven fake reviews. Fake reviews and comments not only drive consumers to buy low-quality products or services, but also erode consumers' long-term confidence in review rating platforms. At present, two main reasons for the low detection accuracy of fake comments in recent studies are: (1) lack of feature learning of emotional intensity of text; (2) the inaccuracy of the identification of topic words in comments. To solve the above problems, we propose a novel identification method based on topic model and Att-BiLSTM mechanism. The proposed method calculates text affective and subjective values using TextBlob, incorporating the topic feature to train the classifier for fake review recognition. Comparative experiments show that the model effect is better than other models.