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Augmenting the global semantic information between words to heterogeneous graph for deception detection

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Abstract

Detecting deceptive reviews can assist customers in grasping the real evaluation of products and services to make better purchase decisions and help companies satisfy timely to their customers’ expectations. Methods based on neural networks for deceptive review detection have made significant progress in recent years. Models using attention mechanisms such as BERT have demonstrated the ability to capture contextual information in review texts. However, their ability to capture global information about the word level is limited. This latter is the strength of Graph Convolutional Networks (GCNs). In this study, we propose a detection model (SGCN-BERT) based on the combination of Semantic Graph Convolutional Network (SGCN) and pre-trained model BERT. During the construction of the heterogeneous review graph, we consider both the co-occurrence relationship and semantic relationship between words to enrich the graph information. The graph embedding of the reviews are obtained through SGCN and input to BERT together with word embeddings. Global and local information containing lexical-semantic interact through different layers of BERT, allowing them to influence and build the final classification representation jointly mutually. Comprehensive tests on four public datasets show that our method outperforms previous methods and has good generalization capability.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2572019BH03).

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  1. School of Information and Computer Engineering, Northeast Forestry University, Wenzheng Street, Harbin, 150006, Heilongjiang Province, China

    Shi Li & Wenfeng Cheng

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  1. Shi Li
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  2. Wenfeng Cheng
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Correspondence to Wenfeng Cheng.

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Li, S., Cheng, W. Augmenting the global semantic information between words to heterogeneous graph for deception detection. Neural Comput & Applic 34, 19079–19090 (2022). https://doi.org/10.1007/s00521-022-07492-y

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