Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection
Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review.
['LM', 'Amazon', 'Yelp']
This means that a large number of fake reviews can be efficiently generated with a desired sentiment by just fine-tuning an LM. The sentiment modeling method had the highest rate for the Amazon database. This was because explicitly modeling sentiment benefits from the additional sentiment information given before the fake reviews are generated. This indicates that explicitly modeling sentiment could be a more efficient way to generate desired sentiment reviews. For the Yelp reviews, fine-tuned GPT-2 was also clearly better than the pretrained GPT-2 and the mLSTM had the highest rate. Further analysis revealed that the mLSTM model performs very well only for food and restaurant reviews but it did not generalize well to other domains or it generates reviews completely outside the context of the original review. This suggests that we need to further explicitly preserve context. (We leave this for future work.)