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Incorporating feature representation into BiLSTM for deceptive review detection

  • Wentao Liu
  • Weipeng JingEmail author
  • Yang Li


Consumers are increasingly influenced by product reviews when purchasing goods or services. At the same time, deceptive reviews usually mislead users. It is inefficient and inaccurate to manually identify deceptive reviews in massive reviews. Therefore, automatically identifying deceptive reviews has become a research trend. Most of existing methods are less effective since they are lack of deeply understanding of reviews. We propose a neural network method with bidirectional long short-term memory (BiLSTM) and feature combination to learn the representation of deceptive reviews. We conduct a large amount of experiments and demonstrate the effectiveness of our proposed method. Specifically, in the mixed-domain detection experiment, the results prove that our model is effective by making comparisons with other neural network-based methods. BiLSTM gives more than 3% improvement in F1 score compared with the most advanced neural network method. Since feature selection plays an important role in this direction, we combine features to improve the performance. Then we get 87.6% F1 value which outperforms the state-of-the-art method. Moreover, in the cross-domain detection experiment, our method achieves 82.4% F1 value which is about 6% higher than the state-of-the-art method on restaurant domain, and it is also robust on doctor domain.


Deceptive review detection Bidirectional long short-term memory neural network Feature combination Representation learning 

Mathematics Subject Classification




The work described in this paper is supported by National Natural Science Foundation of China (61806049), National Natural Science Foundation of China (31770768), the Natural Science Foundation of Heilongjiang Province of China (F2017001), Heilongjiang Province Applied Technology Research and Development Program Major Proje-ct (GA18B301), China State Forestry Administration Forestry Industry Public Welfare Project (201504307) and China Postdoctoral Science Foundation (2017M611407).


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Computer EngineeringNortheast Forestry UniversityHarbinChina

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