SAFER: Sentiment Analysis-Based FakE Review Detection in E-Commerce Using Deep Learning

The problem of fake deceptive reviews has become a threatening aspect for online users in recent years. With the evolution of the online markets, the trend towards fake reviews has increased, mainly to attract or distract customers. Fake reviews have affected both customers and sellers. These reviews consist of writings and spreading misleading information and beliefs. Sentiment analysis was first introduced a few years ago in the e-commerce sector. It is an emerging research area today due to the rapid growth in the e-commerce industry. The biggest challenge in detecting fake reviews is the lack of an effective way to distinguish fake reviews from legitimate reviews. The difference cannot be seen with the naked eye and is, therefore, a severe concern. In this paper, we have applied the bag of words model and glove embedding matrix with a focus on fake reviews. We have used two different feature extraction techniques and three new deep-learning algorithms on text classifications. The experimental analysis with an existing public dataset showed good and better results compared to the traditional machine-learning models.