Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Analysis (1)
- Applied Mathematics (1)
- Applied Statistics (1)
- Artificial Intelligence and Robotics (1)
- Business (1)
-
- Business Analytics (1)
- Business Intelligence (1)
- Business and Corporate Communications (1)
- Computer Law (1)
- Computer Sciences (1)
- Engineering (1)
- Engineering Education (1)
- Law (1)
- Legal Studies (1)
- Mathematics (1)
- Multivariate Analysis (1)
- Numerical Analysis and Computation (1)
- Other Legal Studies (1)
- Other Statistics and Probability (1)
- Probability (1)
- Science and Technology Studies (1)
- Social Statistics (1)
- Social and Behavioral Sciences (1)
- Statistical Methodology (1)
- Statistical Models (1)
- Statistics and Probability (1)
- Technology and Innovation (1)
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia
Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia
SMU Data Science Review
Fake news is defined as a made-up story with an intention to deceive or to mislead. In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research [1] predicts that “By 2022, most people in mature economies will consume more false information than true information”. The exponential increase in production and distribution of inaccurate news presents an immediate need for automatically tagging and detecting such twisted news articles. However, automated detection of fake news is a hard task to accomplish as it requires the model to understand nuances in natural …
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …