Sparse regression of textual analysis

dc.contributorChen, Yuhui
dc.contributorDavis, Cali M.
dc.contributorKwon, Hyun-Kyoung
dc.contributorZhu, Wei
dc.contributor.advisorAmes, Brendan
dc.contributor.authorCarter, Phylisicia N.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2018-12-14T18:12:31Z
dc.date.available2018-12-14T18:12:31Z
dc.date.issued2018
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractWe consider sparse regression techniques as tools for classification of sentiment within Twitter posts. Analysis of Twitter usage suffers from several unique challenges. For example, the 140-character limit severely limits the amount of information contained in each post; this causes most tweets to contain an extremely small subset of the dictionary, presenting challenges for learning schemes based on dictionary usage. To remedy this undersampling issue, we propose usage of penalized regression. Here, we employ logistic regularization to avoid any degeneracy caused by the sparse usage of the dictionary in each tweet, while simultaneously learning which terms are most associated with each sentiment. Accelerated sparse discriminant analysis is also used to combat the issues of degeneracy and overfitting of the training data while providing dimension reduction. As illustrative examples, we employ sparse logistic regression to classify tweets based on the users’ perception of a connection between vaccination and autism, and we examine the Twitter users' sentiment of the use of autonomous cars.en_US
dc.format.extent107 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0003144
dc.identifier.otherCarter_alatus_0004D_13541
dc.identifier.urihttp://ir.ua.edu/handle/123456789/5276
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectApplied mathematics
dc.titleSparse regression of textual analysisen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Mathematics
etdms.degree.disciplineMathematics
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.
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