Improving LDA Topic Models for Microblogs via Tweet Pooling and Automatic Labeling

dc.contributor.authorMehrotra, Rishabh
dc.contributor.authorSanner, Scott
dc.contributor.authorBuntine, Wray
dc.contributor.authorXie, Lexing
dc.coverage.spatialDublin Ireland
dc.date.accessioned2015-12-10T23:18:40Z
dc.date.createdAugust 1 2013
dc.date.issued2013
dc.date.updated2016-02-24T10:57:38Z
dc.description.abstractTwitter, or the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and academic
dc.identifier.urihttp://hdl.handle.net/1885/65728
dc.publisherSIGIR 2013 Workshop proceedings
dc.relation.ispartofseriesWorkshop on Modeling User Behavior for Information Retrieval Evaluation (MUBE 2013 and SIGIR 2013)
dc.subjectKeywords: Automatic labeling; Data preprocessing; Latent dirichlet allocations; LDA; Microblogs; News articles; Topic model; Topic Modeling; Information retrieval; Machinery; Statistics; World Wide Web; Social networking (online) LDA; Microblogs; Topic modeling
dc.titleImproving LDA Topic Models for Microblogs via Tweet Pooling and Automatic Labeling
dc.typeConference paper
local.bibliographicCitation.lastpage4
local.bibliographicCitation.startpage1
local.contributor.affiliationMehrotra, Rishabh, BITS Pilani
local.contributor.affiliationSanner, Scott, College of Engineering and Computer Science, ANU
local.contributor.affiliationBuntine, Wray, College of Engineering and Computer Science, ANU
local.contributor.affiliationXie, Lexing, College of Engineering and Computer Science, ANU
local.contributor.authoruidSanner, Scott, u1817461
local.contributor.authoruidBuntine, Wray, u1817485
local.contributor.authoruidXie, Lexing, u4983843
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1150
local.identifier.doi10.1145/2484028.2484166
local.identifier.scopusID2-s2.0-84883095788
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
01_Mehrotra_Improving_LDA_Topic_Models_for_2013.pdf
Size:
170.68 KB
Format:
Adobe Portable Document Format