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.
|Title of host publication||RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING|
|Number of pages||7|
|Publication status||Published - 01 Sep 2019|