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dc.contributor.authorKannan Ambili, Aardra
dc.date.accessioned2015-07-08T04:30:19Z
dc.date.available2015-07-08T04:30:19Z
dc.date.issued2014-12
dc.identifier.otherkannan-ambili_aardra_201412_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/kannan-ambili_aardra_201412_ms
dc.identifier.urihttp://hdl.handle.net/10724/31445
dc.description.abstractConceptual/Integrative complexity is a construct developed in political psychology and clinical psychology to measure an individual’s ability to consider different perspectives on a particular issue and reach a justifiable conclusion after consideration of said perspectives. Integrative complexity (IC) is usually determined from text through manual scoring, which is time-consuming, laborious and expensive. Consequently, there is a demand for automating the scoring, which could significantly reduce the time, expense and cognitive resources spent in the process. Any algorithm that could achieve the above with a reasonable accuracy could assist in the development of intervention systems for reducing the potential for aggression, systems for recruitment processes and even training personnel for improving group complexity in the corporate world. The proposed approach produced classification accuracies ranging from 75% to 83%, which is a first in the literature for automated scoring of integrative complexity.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectintegrative complexity
dc.subjecttext classification
dc.subjectsemantic similarity
dc.subjectsupport vector machines
dc.titleAutomated scoring of integrative complexity using machine learning and natural language processing
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorKhaled Rasheed
dc.description.committeeKhaled Rasheed
dc.description.committeeWalter Potter
dc.description.committeeAdam Goodie


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