{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T13:40:39Z","timestamp":1772804439252,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Members of a profession frequently show similar personality characteristics. In this research, we leverage recent advances in NLP to compute personal values using a moral values framework, distinguishing between four different personas that assist in categorizing different professions by personal values: \u201cfatherlanders\u201d\u2014valuing tradition and authority, \u201cnerds\u201d\u2014valuing scientific achievements, \u201cspiritualists\u201d\u2014valuing compassion and non-monetary achievements, and \u201ctreehuggers\u201d\u2014valuing sustainability and the environment. We collected 200 YouTube videos and podcasts for each professional category of lawyers, academics, athletes, engineers, creatives, managers, and accountants, converting their audio to text. We also categorize these professions by team player personas into \u201cbees\u201d\u2014collaborative creative team players, \u201cants\u201d\u2014competitive hard workers, and \u201cleeches\u201d\u2014selfish egoists using pre-trained models. We find distinctive personal value profiles for each of our seven professions computed from the words that members of each profession use.<\/jats:p>","DOI":"10.3390\/a18020072","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:18:56Z","timestamp":1738585136000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Do What You Say\u2014Computing Personal Values Associated with Professions Based on the Words They Use"],"prefix":"10.3390","volume":"18","author":[{"given":"Aditya","family":"Jha","sequence":"first","affiliation":[{"name":"MIT System Design Management, 77 Massachusetts Avenue, Cambridge, MA 02142, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-3224","authenticated-orcid":false,"given":"Peter A.","family":"Gloor","sequence":"additional","affiliation":[{"name":"MIT System Design Management, 77 Massachusetts Avenue, Cambridge, MA 02142, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1109\/TCSS.2020.3033302","article-title":"Aspect-based sentiment analysis: A survey of deep learning methods","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Trans. 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