Fake reviews are increasingly prevalent across the Internet. They can be unethical and harmful. They can affect businesses and mislead customers. As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical. In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection. The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.

Online reviews are an essential aspect of online shopping for both customers and retailers. However, many reviews found on the Internet lack in quality, informativeness or helpfulness. In many cases, they lead the customers towards positive or negative opinions without providing any concrete details (e.g., very poor product, I would not recommend it). In this work, we propose a novel unsupervised method for quantifying helpfulness leveraging the availability of a corpus of reviews. In particular, our method exploits three characteristics of the reviews, viz., relevance, emotional intensity and specificity, towards quantifying helpfulness. We perform three rankings (one for each feature above), which are then combined to obtain a final helpfulness ranking. For the purpose of empirically evaluating our method, we use review of four product categories from Amazon review 1 . The experimental evaluation demonstrates the effectiveness of our method in comparison to a recent and state-of-the-art baseline.