2026-09-19T01:48:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1306852026-06-06T00:56:25Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Alex Pentland.
Choi, Seri,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-05-24T19:40:22Z
2021-05-24T19:40:22Z
2021
2021
https://hdl.handle.net/1721.1/130685
1251779073
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 45-47).
Online review platforms have become an essential element of the business industry, providing users in-depth information on businesses and other users' experiences. The purpose of this study is to examine possible bias or discriminatory behaviors in users' rating habits in the Yelp dataset. The Surprise recommender system is utilized to produce expected ratings for the test set, training the model with 75% of the original dataset to learn the rating trends. Then, the ordinary least squares (OLS) linear regression is applied to identify which factors affected the percent change and which categories or locations show more bias than the others. This paper can provide insights into ways that bias can manifest within a dataset due to non-experimental factors such as social psychology; future research into this topic can therefore take these non-experimental factors, such as the discriminatory bias found in Yelp reviews, into consideration in order to reduce bias when utilizing machine learning algorithms.
by Seri Choi.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
47 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
An empirical study identifying bias in Yelp dataset
Thesis
application/pdf
2021-05-24T19:40:22Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Online review platforms have become an essential element of the business industry, providing users in-depth information on businesses and other users' experiences. The purpose of this study is to examine possible bias or discriminatory behaviors in users' rating habits in the Yelp dataset. The Surprise recommender system is utilized to produce expected ratings for the test set, training the model with 75% of the original dataset to learn the rating trends. Then, the ordinary least squares (OLS) linear regression is applied to identify which factors affected the percent change and which categories or locations show more bias than the others. This paper can provide insights into ways that bias can manifest within a dataset due to non-experimental factors such as social psychology; future research into this topic can therefore take these non-experimental factors, such as the discriminatory bias found in Yelp reviews, into consideration in order to reduce bias when utilizing machine learning algorithms.</Abstract>
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