Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
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.
Keywords
Cite
@article{arxiv.2012.13400,
title = {Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training},
author = {Athirai A. Irissappane and Hanfei Yu and Yankun Shen and Anubha Agrawal and Gray Stanton},
journal= {arXiv preprint arXiv:2012.13400},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:1903.08289