Law Commons^™

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 …

We’Ve Come A Long Way (Baby)! Or Have We? Evolving Intellectual Freedom Issues In The Us And Florida, L. Bryan Cooper, A.D. Beman-Cavallaro

Works of the FIU Libraries

This paper analyzes a shifting landscape of intellectual freedom (IF) in and outside Florida for children, adolescents, teens and adults. National ideals stand in tension with local and state developments, as new threats are visible in historical, legal, and technological context. Examples include doctrinal shifts, legislative bills, electronic surveillance and recent attempts to censor books, classroom texts, and reading lists.

Privacy rights for minors in Florida are increasingly unstable. New assertions of parental rights are part of a larger conservative animus. Proponents of IF can identify a lessening of ideals and standards that began after doctrinal fruition in the 1960s …