Browsing by Author "Tek, Ahmet"

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Predicting Yelp Stars Based on Business Attributes
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tek, Ahmet; Arısoy Saraçlar, Ebru
Yelp is a business review website where consumers can comment on a business from their point of view. This allows other consumers to have prior knowledge of the business. Whenever we search something we try and hope to get the most relevant results, and recommender systems can achieve this. Review websites, such as Yelp and TripAdvisor allow users to post online reviews for various businesses, products and services and have been recently shown to have a significant influence on consumer shopping behavior [1]. This paper aims to predict restaurant ratings using their attributes such as alcohol, noise level, Wifi, music, a smoking area and to find the most important attributes for higher ratings. Yelp dataset has lots of information about businesses and consumer behaviors and it is free for academic usage. For these reasons, Yelp dataset has been selected in this project. Machine Learning models have been executed for two-star label classification. Since we aim to find the most important features for a higher rating we only choose 4 and 5-star labels from the dataset. In our research, restaurant rating prediction is implemented as binary-class classification where the class labels are the star ratings. Restaurant attributes are the input features of the classifier. We will investigate Decision Trees, Naive Bayes Classifier, Two-Class Decision Forest, Two-Class Boosted Decision Trees, TwoClass Neural Network, Two-Class Support Vector Machine, Two-Class Logistic Regression and choose the most important 10 attributes resulting in high ratings.