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Stable topic modeling for web science : Granulated LDA. / Koltcov, Sergei; Nikolenko, Sergey I.; Koltsova, Olessia; Bodrunova, Svetlana.

WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference. Association for Computing Machinery, 2016. p. 342-343 (WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Koltcov, S, Nikolenko, SI, Koltsova, O & Bodrunova, S 2016, Stable topic modeling for web science: Granulated LDA. in WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference. WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference, Association for Computing Machinery, pp. 342-343, 8th ACM Web Science Conference, WebSci 2016, Hannover, Germany, 22/05/16. https://doi.org/10.1145/2908131.2908184

APA

Koltcov, S., Nikolenko, S. I., Koltsova, O., & Bodrunova, S. (2016). Stable topic modeling for web science: Granulated LDA. In WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference (pp. 342-343). (WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference). Association for Computing Machinery. https://doi.org/10.1145/2908131.2908184

Vancouver

Koltcov S, Nikolenko SI, Koltsova O, Bodrunova S. Stable topic modeling for web science: Granulated LDA. In WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference. Association for Computing Machinery. 2016. p. 342-343. (WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference). https://doi.org/10.1145/2908131.2908184

Author

Koltcov, Sergei ; Nikolenko, Sergey I. ; Koltsova, Olessia ; Bodrunova, Svetlana. / Stable topic modeling for web science : Granulated LDA. WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference. Association for Computing Machinery, 2016. pp. 342-343 (WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference).

BibTeX

@inproceedings{a74e88e73123498099c77deb999f2d8a,

title = "Stable topic modeling for web science: Granulated LDA",

abstract = "Topic modeling is a powerful tool for analyzing large collections of user-generated web content, but it still suffers from problems with topic stability, which are especially important for social sciences. We evaluate stability for differenttopic models and propose a new model, granulated LDA,that samples short sequences of neighboring words at once. We show that gLDA exhibits very stable results.",

keywords = "Gibbs sampling, Latent Dirichlet allocation, Topic modeling",

author = "Sergei Koltcov and Nikolenko, {Sergey I.} and Olessia Koltsova and Svetlana Bodrunova",

note = "Publisher Copyright: {\textcopyright}2016 Copyright held by the owner/author(s). Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 8th ACM Web Science Conference, WebSci 2016 ; Conference date: 22-05-2016 Through 25-05-2016",

year = "2016",

month = may,

day = "22",

doi = "10.1145/2908131.2908184",

language = "English",

series = "WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference",

publisher = "Association for Computing Machinery",

pages = "342--343",

booktitle = "WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference",

address = "United States",

}

RIS

TY - GEN

T1 - Stable topic modeling for web science

T2 - 8th ACM Web Science Conference, WebSci 2016

AU - Koltcov, Sergei

AU - Nikolenko, Sergey I.

AU - Koltsova, Olessia

AU - Bodrunova, Svetlana

PY - 2016/5/22

Y1 - 2016/5/22

N2 - Topic modeling is a powerful tool for analyzing large collections of user-generated web content, but it still suffers from problems with topic stability, which are especially important for social sciences. We evaluate stability for differenttopic models and propose a new model, granulated LDA,that samples short sequences of neighboring words at once. We show that gLDA exhibits very stable results.

AB - Topic modeling is a powerful tool for analyzing large collections of user-generated web content, but it still suffers from problems with topic stability, which are especially important for social sciences. We evaluate stability for differenttopic models and propose a new model, granulated LDA,that samples short sequences of neighboring words at once. We show that gLDA exhibits very stable results.

KW - Gibbs sampling

KW - Latent Dirichlet allocation

KW - Topic modeling

UR - http://www.scopus.com/inward/record.url?scp=84976339906&partnerID=8YFLogxK

U2 - 10.1145/2908131.2908184

DO - 10.1145/2908131.2908184

M3 - Conference contribution

T3 - WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference

SP - 342

EP - 343

BT - WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference

PB - Association for Computing Machinery

Y2 - 22 May 2016 through 25 May 2016

ER -

ID: 7604123