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OLLDA: A Supervised and Dynamic Topic Mining Framework in Twitter
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.ORCID-id: 0000-0002-7786-9551
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.ORCID-id: 0000-0002-4722-0823
2015 (engelsk)Inngår i: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015, s. 1354-1359Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Analyzing media in real-time is of great importance with social media platforms at the epicenter of crunching, digesting and disseminating content to individuals connected to these platforms. Within this context, topic models, specially LDA, have gained strong momentum due to their scalability, inference power and their compact semantics. Although, state of the art topic models come short in handling streaming large chunks of data arriving dynamically onto the platform, thus hindering their quality of interpretation as well as their adaptability to information overload. As a result, in this manuscript we propose for a labelled and online extension to LDA (OLLDA), which incorporates supervision through external labeling and capability of quickly digesting real-time updates thus making it more adaptive to Twitter and platforms alike. Our proposed extension has capability of handling large quantities of newly arrived documents in a stream, and at the same time, is capable of achieving high topic inference quality given the short and often sloppy text of tweets. Our approach mainly uses an approximate inference technique based on variational inference coupled with a labeled LDA model. We conclude by presenting experiments using a one year crawl of Twitter data that shows significantly improved topical inference as well as temporal user profile classification when compared to state of the art baselines.

sted, utgiver, år, opplag, sider
2015. s. 1354-1359
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-192057DOI: 10.1109/ICDMW.2015.132ISI: 000380556700183Scopus ID: 2-s2.0-84964797270ISBN: 978-1-4673-8493-3 (tryckt)OAI: oai:DiVA.org:kth-192057DiVA, id: diva2:958261
Konferanse
IEEE 15th International Conference on Data Mining Workshops (ICDMW), NOV 14-17, 2015, ATlantic city, NJ
Merknad

QC 20160906

Tilgjengelig fra: 2016-09-06 Laget: 2016-09-05 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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