Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training

• URL: http://arxiv.org/abs/2012.13400v1
• Date: Thu, 24 Dec 2020 18:59:51 GMT
• Title: Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
• Authors: Athirai A. Irissappane, Hanfei Yu, Yankun Shen, Anubha Agrawal, Gray Stanton
• Abstract summary: We propose an adversarial training mechanism for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited.
• Score: 1.8899300124593648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train spam detection models. We propose an adversarial training mechanism leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited. The proposed model can also generate synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing additional labeled data during training.

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