APJIS Asia Pacific Journal of Information Systems

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The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus Indexed Journal,
is published by the Korea Society of Management Information Systems (KMIS),
which is the largest professional institute in the field of information systems in Korea.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Hee-Woong Kim

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Past Issue

Date March 2019
Vol. No. Vol. 29 No. 1
DOI https://doi.org/10.14329/apjis.2019.29.1.35
Page 35~49
Title Applications of Machine Learning Models on Yelp Data
Author Ruchi Singh, Jongwook Woo
Keyword Machine learning, Yelp, Recommender, Predictive analytics, Text analysis
Abstract The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML.The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.


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