Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection
Table 5: Fluency of reviews (in MOS). Bold font indicates highest score.
['Model', 'Native', 'Amazon Non-native', 'Overall', 'Native', 'Yelp Non-native', 'Overall']
The fine-tuning improved the fluency compared with that of the reviews generated by the pre-trained GPT-2. This suggests that an attack can be made more effective by simply fine-tuning existing models. For the Amazon dataset, the reviews generated by explicitly modeling the sentiment (sentiment modeling) had the highest overall score, followed by those generated by the fine-tuned GPT-2 model. Interestingly, the scores for all fake review were higher than that for the original review. This observation is similar to that of Yao et al. This observation does not hold for the Yelp database — the score for the original reviews is higher than those for the fake ones. Among the fake review generation models, the fine-tuned GPT-2 model had the highest score (3.30).