"Best Dinner Ever!!!": Automatic Generation of Restaurant Reviews with LSTM-RNN

Consumer reviews are an important information resource for people and a fundamental part of everyday decision-making. Product reviews have an economical relevance which may attract malicious people to commit a review fraud, by writing false reviews. In this work, we investigate the possibility of generating hundreds of false restaurant reviews automatically and very quickly. We propose and evaluate a method for automatic generation of restaurant reviews tailored to the desired rating and restaurant category. A key feature of our work is the experimental evaluation which involves human users. We assessed the ability of our method to actually deceive users by presenting to them sets of reviews including a mix of genuine reviews and of machine-generated reviews. Users were not aware of the aim of the evaluation and the existence of machine-generated reviews. As it turns out, it is feasible to automatically generate realistic reviews which can manipulate the opinion of the user.

I. Introduction

Online product reviews play a crucial role in both the electronic and conventional commerce [1]. Many websites and user forums allow online communities to share their experience about products, touristic destinations, cultural offerings, and so on. Such information may be very useful to both users interested in a certain item and sellers interested in increasing their revenue. Since users tend to trust the opinion of other users, online reviews strongly influence decisions.