Employee Ratings and Reviews Data from Glassdoor

ABSTRACT This paper presents the employee ratings and reviews data from Glassdoor and the R codes used to collect, clean, and organize the data. We collect three types of information for each Glassdoor review: review metrics, content, and reviewer information. We also calculate some commonly used textual metrics, such as sentiment, readability, the number of uncertainty words, etc. The datasets include necessary identifiers that can connect to other financial data sources. All the variables and metrics are provided at the review level, which enables researchers to aggregate the data from different levels and angles. As a demonstrative example, we use a simple word list to measure how often employees mention COVID-19 in review comments. The R codes provided as an RStudio project are self-contained and can also be modified and applied to other data sources of interest.

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Publication Details

Journal
Journal of Information Systems
Published
2024-08-09
DOI
https://doi.org/10.2308/isys-2023-003
Citations
5
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
1.74
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article

Employee Ratings and Reviews Data from Glassdoor

Yiyun Li, Zhilei Qiao, Mi Zhou, Bowen Shi
5 citations
Journal of Information Systems
Sentiment Analysis and Opinion Mining
1.74
article

Employee Ratings and Reviews Data from Glassdoor

Yiyun Li, Zhilei Qiao, Mi Zhou, Bowen Shi
article en
5 citations

Abstract

ABSTRACT This paper presents the employee ratings and reviews data from Glassdoor and the R codes used to collect, clean, and organize the data. We collect three types of information for each Glassdoor review: review metrics, content, and reviewer information. We also calculate some commonly used textual metrics, such as sentiment, readability, the number of uncertainty words, etc. The datasets include necessary identifiers that can connect to other financial data sources. All the variables and metrics are provided at the review level, which enables researchers to aggregate the data from different levels and angles. As a demonstrative example, we use a simple word list to measure how often employees mention COVID-19 in review comments. The R codes provided as an RStudio project are self-contained and can also be modified and applied to other data sources of interest.

Journal of Information SystemsVol. 38(3)
Idaho State University (US), Central University of Finance and Economics (CN), Virginia Commonwealth University (US), University of Alabama at Birmingham (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Sentiment Analysis and Opinion Mining
1.74
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Employee Ratings and Reviews Data from Glassdoor — Yiyun Li, Zhilei Qiao, et al. · Journal of Information Systems (2024) | TGRS Research Map | TGRS