Toward a Natural Language Processing-based Model for Workplace Culture Scoring

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Narayanan, Srinidhi

Advisor dc:contributor.advisor

• Kala, Namrata

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164847

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164847