Open Access

ARTICLE

Spam Detection in Reviews Using LSTM-Based Multi-Entity Temporal Features

by Lingyun Xiang^1,2,3, Guoqing Guo², Qian Li⁴, Chengzhang Zhu^5,*, Jiuren Chen⁶, Haoliang Ma²

1 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
3 Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, 410114, China
4 Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, Australia
5 Academy of Military Sciences, Beijing, 100091, China
6 Science and Technology on Test Physics and Numerical Mathematic Laboratory, Beijing, 100094, China

* Corresponding Author: Chengzhang Zhu. Email:

Intelligent Automation & Soft Computing 2020, 26(6), 1375-1390. https://doi.org/10.32604/iasc.2020.013382

Received 04 August 2020; Accepted 12 September 2020; Issue published 24 December 2020

Abstract

Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and train a Long-Short-Term Memory (LSTM) model to learn the temporal features of every review in the user and product entities. Finally, we train a classifier using all of the learned temporal features in order to predict whether a particular review is spam. Experimental results demonstrate that the proposed method can effectively extract the temporal features from historical activities, and can further jointly analyze the activity trajectories from multiple entities. Thus, the proposed method significantly improves the spam detection accuracy.

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Keywords

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