dc.contributor.advisor | Kim, Jeong-Nam | |
dc.contributor.author | Lee, Hyelim | |
dc.date.accessioned | 2023-07-14T15:17:42Z | |
dc.date.available | 2023-07-14T15:17:42Z | |
dc.date.issued | 2023-08-03 | |
dc.identifier.uri | https://hdl.handle.net/11244/337930 | |
dc.description.abstract | The current dissertation aims to develop a Machine Learning (ML) method for automating the assessment of digital public relations by incorporating the Organization-Public Relationship Assessment (OPRA) developed from the public relations theory. The study targets customers/consumers and employees. For methods, Natural Language Processing (NLP) techniques, specifically text-embedding and classification, are used to analyze the crawled data and three survey data. The results demonstrate that TF-IDF, BERT embedding, and the SVM classification model perform best. The case study outcomes using TripAdvisor and Glassdoor review data validate the previous results. This dissertation project can serve as a pioneering effort to enhance the theoretical foundation of most current data analytics tools in public relations. | en_US |
dc.language | en_US | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | Public Relations | en_US |
dc.subject | Strategic Communication | en_US |
dc.subject | Data Analytics | en_US |
dc.title | Theory-enhanced automation of the digital publics' relationship assessments | en_US |
dc.contributor.committeeMember | Zhang, Xiaochen Angela | |
dc.contributor.committeeMember | Kerr, Robert | |
dc.contributor.committeeMember | Park, Ji Hwan | |
dc.contributor.committeeMember | Jang, Yun | |
dc.date.manuscript | 2023-07-12 | |
dc.thesis.degree | Ph.D. | en_US |
ou.group | Gaylord College of Journalism and Mass Communication | en_US |
shareok.orcid | 0000-0001-9032-9835 | en_US |
shareok.nativefileaccess | restricted | en_US |