A deceptive reviews detection model: Separated training of multi-feature learning and classification

The increasing online reviews play an essential role in the e-commerce platform, which profoundly affects the purchase decisions of consumers. However, rampant dishonest sellers manipulate other buyers or robots to post deceptive reviews for profit. Recently, the detection of deceptive reviews has attracted general research attention, which mainly comprises two directions, traditional methods based on statistics and intelligent methods based on neural networks. These methods use a single feature or multiple features for classifier design. To make full use of different features for better feature representation of detecting deceptive reviews, this paper proposes a new feature fusion strategy and verifies its performance by comparing it with other feature fusion strategies. First, we utilize three independent models for feature extraction: the TextCNN, the Bidirectional Gated Recurrent Unit (GRU), and the Self-Attention are used to learn local semantic features, temporal semantic features, and weighted semantic features of reviews, respectively. Secondly, after obtaining different feature representations from the fully connected layers of these three models, we concatenate them together to form the final documental representation. Finally, we use a full connection layer and the sigmoid function to further learn and complete deceptive review detection. Experiments on three balanced and unbalanced in-domain small datasets (hotel, restaurant, doctor) and mixed-domain datasets show that our model is superior to baselines. Experiments on large-scale data with various imbalanced proportions verify the effectiveness of our method. We also analyze the results of different datasets from the perspective of part of speech to improve the model's interpretability.