Comparison of algorithms for Twitter sentiment analysis

Simple item page
dc.contributor.advisorCaramanis, Constantine
dc.creatorWhipple, Adam Lane
dc.creator.orcid0000-0002-3566-3459
dc.date.accessioned2017-07-10T22:16:00Z
dc.date.available2017-07-10T22:16:00Z
dc.date.issued2017-05
dc.date.submittedMay 2017
dc.date.updated2017-07-10T22:16:00Z
dc.description.abstractSentiment Analysis has gained attention in recent years owing to the massive increase in personal statements made at the individual level, spread across vast geographic and demographic ranges. That data has become vastly more accessible as micro-blog sites such as Twitter and Facebook have released public, free interfaces. This research seeks to understand the processes behind Sentiment Analysis and to compare statistical methodologies for classifying Twitter sentiments.
dc.description.departmentElectrical and Computer Engineering
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2HT2GS58
dc.identifier.urihttp://hdl.handle.net/2152/60372
dc.language.isoen
dc.subjectSentiment Analysis
dc.subjectTwitter
dc.subjectSVM
dc.subjectNaive Bayes
dc.subjectSGD classification
dc.titleComparison of algorithms for Twitter sentiment analysis
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
WHIPPLE-MASTERSREPORT-2017.pdf
Size:
166.66 KB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description:
Download

Collections

UT Electronic Theses and Dissertations