•  
  •  
 

The SMU Data Science Review is a peer-reviewed electronic journal that promotes data-driven scientific discovery and welcomes experimental and theoretical research on advanced data science technologies and their real world applications.

See the Aims and Scope for a complete coverage of the journal.

Current Issue: Volume 1, Number 3 (2018)

Articles

PDF

Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis
Jethin Abraham, Daniel Higdon, John Nelson, and Juan Ibarra

PDF

Yelp’s Review Filtering Algorithm
Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, and Daniel W. Engels

PDF

Enhancing Trust in the Cryptocurrency Marketplace: A Reputation Scoring Approach
Dan Freeman, Tim McWilliams, Sudip Bhattacharyya, Craig Hall, and Pablo Peillard

PDF

Minimizing the Perceived Financial Burden Due to Cancer
Hassan Azhar, Zoheb Allam, Gino Varghese, Daniel W. Engels, and Sajiny John

PDF

Predicting National Basketball Association Success: A Machine Learning Approach
Adarsh Kannan, Brian Kolovich, Brandon Lawrence, and Sohail Rafiqi

PDF

Text Enhanced Recommendation System Model Based on Yelp Reviews
Peter Kouvaris, Ekaterina Pirogova, Hari Sanadhya, Albert Asuncion, and Arun Rajagopal

PDF

Fake News Detection: A Deep Learning Approach
Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, and Nibrat Lohia

PDF

Opencrimemapping.org: An Online Tool for Visualizing Crime
Michael Crowder, Lauren Darr, Gerardo Garza, and Brent Allen

PDF

Overcoming Small Data Limitations in Heart Disease Prediction by Using Surrogate Data
Alfeo Sabay, Laurie Harris, Vivek Bejugama, and Karen Jaceldo-Siegl