Toward a Natural Language Processing-based Model for Workplace Culture Scoring
Name
narayanan-srinaray-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
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1.35 MB
Format
Adobe PDF
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d2c5dd274e24c0af765418dff56cf5d3
Author(s)
Narayanan, Srinidhi
Advisor(s)
Kala, Namrata
Date Issued
September 2025
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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