{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/164847"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/164847","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Toward a Natural Language Processing-based Model for Workplace Culture Scoring","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_html":"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_has_math":false,"creators":["Narayanan, Srinidhi"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Kala, Namrata"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-09","date_published":"2025-09","updated_at":"2026-07-22T22:20:47Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/164847","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kala, Namrata"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Narayanan, Srinidhi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-12T17:14:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-12T17:14:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/164847"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["MNG"]},{"key":"dc:title","label":"Title","values":["Toward a Natural Language Processing-based Model for Workplace Culture Scoring"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kala, Namrata"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Narayanan, Srinidhi"],"dc:date.accessioned":["2026-02-12T17:14:17Z"],"dc:date.available":["2026-02-12T17:14:17Z"],"dc:date.issued":["2025-09"],"dc:description.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."],"dc:description.degree":["MNG"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/164847"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Toward a Natural Language Processing-based Model for Workplace Culture Scoring"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:20:47Z"}