Show simple item record

dc.contributor.advisorWhitaker, Lyn R.
dc.contributor.authorCairoli, Christine M.
dc.dateSep-17
dc.date.accessioned2017-11-07T23:39:01Z
dc.date.available2017-11-07T23:39:01Z
dc.date.issued2017-09
dc.identifier.urihttp://hdl.handle.net/10945/56109
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractThousands of Navy survey free text comments are overlooked every year because reading and interpreting comments is expensive, time consuming, and subjective. Valuable information from these comments is not being utilized to make important Navy decisions. We provide a new procedure to automate the identification of primary topics in short, jargon laced, topic based survey comments by applying a label to each comment and then using those labels to bin comments into operationally meaningful categories. We apply this method to the Navy Retention Survey to provide the Chief of Naval Personnel with an objective analysis of the questions Why are sailors leaving? and What will make sailors stay on active duty? Furthermore, we introduce an implementation of this method using the Demographic Analysis of Responses Tool for Surveys (DARTS), which allows us to filter comment bins using the over 100 demographic and military status elements associated with each sailor. By targeting critically undermanned specialties, the reports generated with this tool provide quantifiable results that allow retention policy makers the ability to review, modify, and create relevant incentives to retain critically talented sailors to meet fiscal year end strength and operational requirements.en_US
dc.description.urihttp://archive.org/details/categorizationof1094556109
dc.publisherMonterey, California: 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. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted.en_US
dc.titleCategorization of survey text utilizing natural language processing and demographic filteringen_US
dc.typeThesisen_US
dc.contributor.secondreaderAnglemyer, Andrew T.
dc.contributor.departmentOperations Research (OR)
dc.subject.authorNavy retentionen_US
dc.subject.authorsurvey commentsen_US
dc.subject.authorcomment labelingen_US
dc.subject.authortext analysisen_US
dc.subject.authornatural language processingen_US
dc.description.recognitionOutstanding Thesis
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record