Publication Date

Spring 2018

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science


User opinions on websites like Amazon, Yelp, and TripAdvisor are a key input for consumers when figuring out what to purchase, or where and what to eat. This means that in order for such websites to provide a better service to their customers, they must guard against fake and targeted reviews. Detecting such users and reviews automatically is a very complex multi-step process, and there is no direct mechanism for solving the problem reliably. Multiple AI and Machine Learning algorithms are coupled together when examining user reviews in determining if a review is fake or not. In this project we propose one such mechanism, which examines past user reviews to detect abnormalities, if any, signaling that they should be looked at more thoroughly from more dimensions. We do so by combining existing sentiment analysis techniques and pattern matching. In order to gain more insight into a review, we break it down into sentences and produce a sentiment value for each one, allowing us to represent a review as a sentiment vector. The sentiment vector then allows us to match various sized tuples against other reviews from the user and compute abnormality scores.