A Review Structure Based Ensemble Model for Deceptive Review Spam
AbstractConsumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features of spam reviews. In this paper, we propose a novel model, which splits a review into three parts: first sentence, middle context, and last sentence, based on the discovery that the first and last sentence express stronger emotion than the middle context. Then, the model uses four independent bidirectional long-short term memory (LSTM) models to encode the beginning, middle, end of a review and the whole review into four document representations. After that, the four representations are integrated into one document representation by a self-attention mechanism layer and an attention mechanism layer. Based on three domain datasets, the results of in-domain and mix-domain experiments show that our proposed method performs better than the compared methods. View Full-Text
Share & Cite This Article
Zeng, Z.-Y.; Lin, J.-J.; Chen, M.-S.; Chen, M.-H.; Lan, Y.-Q.; Liu, J.-L. A Review Structure Based Ensemble Model for Deceptive Review Spam. Information 2019, 10, 243.
Zeng Z-Y, Lin J-J, Chen M-S, Chen M-H, Lan Y-Q, Liu J-L. A Review Structure Based Ensemble Model for Deceptive Review Spam. Information. 2019; 10(7):243.Chicago/Turabian Style
Zeng, Zhi-Yuan; Lin, Jyun-Jie; Chen, Mu-Sheng; Chen, Meng-Hui; Lan, Yan-Qi; Liu, Jun-Lin. 2019. "A Review Structure Based Ensemble Model for Deceptive Review Spam." Information 10, no. 7: 243.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.