Sentiment and Emotion Based Representations for Fake Reviews Detection

Abstract

Fake reviews are increasingly prevalent across the Internet. They can be unethical as well as harmful. They can affect businesses and mislead individual customers. As the opinions on the Web are increasingly used the detection of fake reviews has become more and 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 review detection. We perform empirical studies over three real world datasets and 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.

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