APPLYING MACHINE LEARNING AND SENTIMENT ANALYSIS TO ASSESS THE HEALTH OF MARINE CORPS CULTURE

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dc.contributor.advisorMacKinnon, Douglas J.
dc.contributor.advisorZhao, Ying
dc.contributor.authorIrby, Deondra I.
dc.contributor.departmentInformation Sciences (IS)
dc.date.accessioned2024-08-19T16:33:25Z
dc.date.available2024-08-19T16:33:25Z
dc.date.issued2024-06
dc.description.abstractThe Marine Corps' commitment to maintaining a robust Esprit de Corps faces challenges from persistent and pressing issues for commanders like military suicides, mental health concerns, sexual harassment, assault, and equal opportunity complaints. This thesis explores the capability and usefulness of Large Language Models (LLMs) to enhance and improve command climate analysis within the Marine Corps by harnessing Natural Language Processing (NLP) to examine free-text responses in the Defense Equal Opportunity Climate Survey (DEOCS). This research rigorously compares the performance of LLMs against traditional machine learning models with regard to sentiment analysis and topic extraction. Our findings reveal that LLMs can provide more accurate and nuanced insights into free text responses of command climate surveys. LLMs also have the potential to expedite the analysis process and provide trend analysis on surveys aggregated over time and throughout commands using these methods along with the proper computational power. These advantages are critical for identifying key areas of concern and fostering troop welfare and retention across the Department of Defense. The deployment of these advanced NLP techniques found in LLMs can represent a significant leap forward in identifying operational efficiencies and can improve strategic decision-making within military contexts.en_US
dc.description.distributionstatementDistribution Statement A. Approved for public release: Distribution is unlimited.en_US
dc.description.serviceCaptain, United States Marine Corpsen_US
dc.identifier.curriculumcode595, Information Warfare
dc.identifier.thesisid39986
dc.identifier.urihttps://hdl.handle.net/10945/73147
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.authornatural language processingen_US
dc.subject.authorNLPen_US
dc.subject.authorsentiment analysisen_US
dc.subject.authortopic extractionen_US
dc.subject.authorBERTopicen_US
dc.subject.authortopic modelingen_US
dc.subject.authorMarine Corps cultureen_US
dc.subject.authorCommand Climate Surveyen_US
dc.subject.authorDEOCSen_US
dc.subject.authorlarge language modelsen_US
dc.subject.authorLlama 2en_US
dc.subject.authorOrange3en_US
dc.titleAPPLYING MACHINE LEARNING AND SENTIMENT ANALYSIS TO ASSESS THE HEALTH OF MARINE CORPS CULTUREen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineInformation Warfare Systems Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.nameMaster of Science in Information Warfare Systems Engineeringen_US
relation.isDepartmentOfPublication74f4d405-0bff-4b6e-9446-edae3a8b11bb
relation.isDepartmentOfPublication.latestForDiscovery74f4d405-0bff-4b6e-9446-edae3a8b11bb
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