Abstract
With the popularization of social software and e-business in recent years, more and more consumers like to share their consumption experiences on social networks and refer to other consumers' reviews and opinions when making consumption decisions. Online reviews have become an essential part of browsing on websites such as shopping, and people's reliance on informative reviews have contributed to the rise of fake reviews. The traditional classification method is affected by the label dataset, which is not only time-consuming, laborious, and subjective, but also the extraction of artificial features also affects the classification accuracy. Due to the relative length of the online text, the possibility of the classifier losing important information increases, this weakens the model’s detection capability. To solve this aforementioned problem, a semi-supervised Generative Adversarial Network (AspamGAN) fake reviews detection method incorporating an attention mechanism is proposed. Using labeled and unlabeled data to correctly learn input distributions, the features required for classification are automatically discovered using deep neural networks, providing better prediction accuracy for online reviews. The approach includes attention mechanisms in the classifier to obtain an adequate semantic representation and relies on a limited dataset of labeled data to detect false reviews, and is applied on the TripAdvisor dataset. Experimental results show that the proposed algorithm outperforms state-of-the-art semi-supervised fake review detection techniques when the label dataset is limited.
References
Jindal N, Liu B, Lim E P. Finding unusual review patterns using unexpected rules[C]//Proceedings of the 19th ACM international conference on information and knowledge management. 2010: 1549-1552.
Crowd Learning Hub. https://learn.g2crowd.com/customer-reviews-statistics. 2018.
Rayana S, Akoglu L. Collective opinion spam detection: Bridging review networks and metadata[C]//Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining. 2015: 985-994.
Li Y, Pan Q, Wang S, et al. A generative model for category text generation[J]. Information Sciences, 2018, 450: 301-315.
Ott M, Choi Y, Cardie C, et al. Finding deceptive opinion spam by any stretch of the imagination[J]. Proceedings of ACL, 2011, pp. 309-319
Li F H, Huang M, Yang Y, et al. Learning to identify review spam[C]//Twenty-second international joint conference on artificial intelligence. 2011.
Hernández-Fusilier D, Guzmán-Cabrera R, Montes-y-Gómez M, et al. Using PU-learning to detect deceptive opinion spam[C]//Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, USA. 2013: 38-45.
Li H, Liu B, Mukherjee A, et al. Spotting fake reviews using positive-unlabeled learning[J]. Computación y Sistemas, 2014, 18(3): 467-475.
Fedus W, Goodfellow I, Dai A M. MaskGAN: Better text generation via filling in the_[J]. arXiv preprint arXiv:1801.07736, 2018.
Stanton G, Irissappane A A. GANs for semi-supervised opinion spam detection[J]. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19). 2020: 5204-5210.
Jindal N, Liu B. Opinion spam and analysis[C]//Proceedings of the 2008 international conference on web search and data mining. 2008: 219-230.
Ahmed M. Elmogy,Usman Tariq,Ammar Mohammed,Atef Ibrahim. Fake Reviews Detection using Supervised Machine Learning[J]. International Journal of Advanced Computer Science and Applications (IJACSA), 2021, 12(1): 601-606.
Feng S, Banerjee R, Choi Y. Syntactic stylometry for deception detection[C]//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2012: 171-175.
Li H, Chen Z, Mukherjee A, et al. Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns[C]//ICWSM. 2015: 634-637.
Guo Z, Tang L, Guo T, et al. Deep graph neural network-based spammer detection under the perspective of heterogeneous cyberspace[J]. Future generation computer systems, 2021, 117: 205-218.
Akoglu, L., Chandy, R., & Faloutsos, C. (2021). Opinion Fraud Detection in Online Reviews by Network Effects. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 2-11.
Anand, C. "Comparison of Stock Price Prediction Models using Pre-trained Neural Networks." Journal of Ubiquitous Computing and Communication Technologies (UCCT) 3, no. 02 (2021): 122-134.
Kottursamy, Kottilingam. "A review on finding efficient approach to detect customer emotion analysis using deep learning analysis." Journal of Trends in Computer Science and Smart Technology 3, no. 2 (2021): 95-113.
Brahmane, Anilkumar V., and B. Chaitanya Krishna. "A Novel Approach for Gigantic Data Examination Utilizing the Apache Spark and Significant Learning." In International Conference on Inventive Computation Technologies, pp. 874-882. Springer, Cham, 2019.
Ren Y, Ji D. Neural networks for deceptive opinion spam detection: An empirical study[J]. Information Sciences, 2017, 385: 213-224.
Lai S, Xu L, Liu K, et al. Recurrent convolutional neural networks for text classification[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015.
Hassan R, Islam M R. Detection of fake online reviews using semi-supervised and supervised learning[C]//2019 International conference on electrical, computer and communication engineering (ECCE). IEEE, 2019: 1-5.
Yu L, Zhang W, Wang J, et al. Seqgan: Sequence generative adversarial nets with policy gradient[C]//Thirty-first AAAI conference on artificial intelligence. 2017.
Tuan Y L, Lee H Y. Improving conditional sequence generative adversarial networks by stepwise evaluation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 27(4): 788-798.
Millán-Arias C C, Fernandes B J T, Cruz F, et al. A robust approach for continuous interactive actor-critic algorithms[J]. IEEE Access, 2021, 9: 104242-104260.
Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation[J]. arXiv preprint arXiv:1508.04025, 2015,1412-1421.
Guo Q, Qiu X, Liu P, Xue X Y. Multi-scale self-attention for text classification[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(05): 7847-7854.
Hu Z, Yang Z, Liang X, et al. Toward controlled generation of text[C]//International Conference on Machine Learning. PMLR, 2017: 1587-1596.
Sutton R S, McAllester D A, Singh S P, et al. Policy gradient methods for reinforcement learning with function approximation[C]//NIPs. 1999, 99: 1057-1063.
Guo J, Lu S, Cai H, et al. Long text generation via adversarial training with leaked information[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).
Grover A, Dhar M, Ermon S. Flow-gan: Combining maximum likelihood and adversarial learning in generative models[C]//Thirty-second AAAI conference on artificial intelligence. 2018.