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Full-Text Articles in Business Intelligence
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
SMU Data Science Review
This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …
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 …
Consumer Welfare And Price Discrimination: A Fine Line, Marie Wallmark, Eyal Greenberg, Dan Engels
Consumer Welfare And Price Discrimination: A Fine Line, Marie Wallmark, Eyal Greenberg, Dan Engels
SMU Data Science Review
Traditionally, it was not feasible for businesses to determine the maximum price the buyer was willing to pay, but with the availability of big data and the deployment of sophisticated algorithms, with a great degree of precision businesses can ascertain the maximum willingness price. Some forms of price discrimination are prohibited under the Robinson-Patman Act of Antitrust (1890), provided demographic characteristics such as race and gender are the determining factors. The problem with this interpretation is that sellers are not transparent about what factors are taken into consideration when determining price. Current laws are either limited in their interpretation or …