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Topic modeling on Amazon MTurk

dc.contributor.advisorYue, Chuan
dc.contributor.authorMiller, Riley
dc.date.accessioned2020-06-07T10:13:29Z
dc.date.accessioned2022-02-03T13:19:59Z
dc.date.available2020-06-07T10:13:29Z
dc.date.available2022-02-03T13:19:59Z
dc.date.issued2020
dc.identifierMiller_mines_0052N_11961.pdf
dc.identifierT 8939
dc.identifier.urihttps://hdl.handle.net/11124/174131
dc.descriptionIncludes bibliographical references.
dc.description2020 Spring.
dc.description.abstractCrowdsourcing is an advancing job market and has been the recent focus of many researchers to help improve crowdsourcing platforms for both crowd workers and requesters.To better understand the content of HITs on Amazon MTurk and to advance further research, extensive topic modeling was performed on a dataset that included HIT titles, descriptions, and previously unexplored in crowdsourcing research: HIT previews.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2020 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectanalysis
dc.subjectHIT
dc.subjecttopic modeling
dc.subjectcrowdsourcing
dc.subjectAmazon
dc.subjectMTurk
dc.titleTopic modeling on Amazon MTurk
dc.typeText
dc.contributor.committeememberWilliams, Thomas
dc.contributor.committeememberWang, Hua
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado School of Mines


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