2026-09-18T19:13:09Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1648472026-02-13T03:49:28Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kala, Namrata
Narayanan, Srinidhi
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2026-02-12T17:14:17Z
2026-02-12T17:14:17Z
2025-09
2025-09-15T14:56:40.257Z
https://hdl.handle.net/1721.1/164847
Workplace culture plays a key role in a firm’s success, impacting employee engagement, productivity, and overall performance. To this end, effectively — and in particular quantitatively — measuring culture provides several advantages to firms and those studying the behavior of firms. In this project, I propose a natural language processing (NLP) framework to generate culture scores for individual employee reviews, leveraging text data from Glassdoor. I combine topic modeling to identify interpretable cultural themes with self-supervised learning to generate and predict review-level scores from sentiment and topic relevance measures. The scores show moderate predictive accuracy against user-entered Glassdoor Culture & Values ratings and moderate correlation with external culture scores from Revelio Labs. I uncover some heterogeneity in model performance across countries, suggesting that employee perceptions of culture may differ across geographic contexts.
MNG
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Toward a Natural Language Processing-based Model for Workplace Culture Scoring
Thesis
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Master
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unknown
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<Abstract>Workplace culture plays a key role in a firm’s success, impacting employee engagement, productivity, and overall performance. To this end, effectively — and in particular quantitatively — measuring culture provides several advantages to firms and those studying the behavior of firms. In this project, I propose a natural language processing (NLP) framework to generate culture scores for individual employee reviews, leveraging text data from Glassdoor. I combine topic modeling to identify interpretable cultural themes with self-supervised learning to generate and predict review-level scores from sentiment and topic relevance measures. The scores show moderate predictive accuracy against user-entered Glassdoor Culture & Values ratings and moderate correlation with external culture scores from Revelio Labs. I uncover some heterogeneity in model performance across countries, suggesting that employee perceptions of culture may differ across geographic contexts.</Abstract>
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