Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Tracking Online Trend Locations using a Geo-Aware Topic Model
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In automatically categorizing massive corpora of text, various topic models have been applied with good success. Much work has been done on applying machine learning and NLP methods on Internet media, such as Twitter, to survey online discussion. However, less focus has been placed on studying how geographical locations discussed in online fora evolve over time, and even less on associating such location trends with topics. Can online discussions be geographically tracked over time? This thesis attempts to answer this question by evaluating a geo-aware Streaming Latent Dirichlet Allocation (SLDA) implementation which can recognize location terms in text. We show how the model can predict time-dependent locations of the 2016 American primaries by automatic discovery of election topics in various Twitter corpora, and deduce locations over time.

Place, publisher, year, edition, pages
2016. , 62 p.
Keyword [en]
geo-aware topic model lda latent dirichlet geographic location
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-190887OAI: oai:DiVA.org:kth-190887DiVA: diva2:953540
External cooperation
FOI
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Presentation
2016-06-16, KTH D building, KTH, Stockholm, 05:10 (English)
Supervisors
Examiners
Available from: 2016-08-26 Created: 2016-08-18 Last updated: 2016-09-30Bibliographically approved

Open Access in DiVA

fulltext(1778 kB)89 downloads
File information
File name FULLTEXT01.pdfFile size 1778 kBChecksum SHA-512
4250e0655ab8c2725dd35e0ef5a98d08c8aaaeaf545f23dc9946e4a88a1fb8c26be274b9ba75f274330d9f42036bc70818829dca2c0de67824f6d12c3a6d78d7
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 89 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 306 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf