Clustering scientific documents with topic modeling

C-K Yau, A Porter, N Newman, Arho Suominen (Corresponding Author)

    Research output: Contribution to journalArticleScientificpeer-review

    72 Citations (Scopus)

    Abstract

    Topic modeling is a type of statistical model for discovering the latent "topics" that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities
    Original languageEnglish
    Pages (from-to)767-786
    JournalScientometrics
    Volume100
    Issue number3
    DOIs
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed

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    Learning systems
    text analysis
    learning
    Statistical Models

    Keywords

    • Topic modeling
    • text analysis
    • latent dirichlet allocation

    Cite this

    Yau, C-K ; Porter, A ; Newman, N ; Suominen, Arho. / Clustering scientific documents with topic modeling. In: Scientometrics. 2014 ; Vol. 100, No. 3. pp. 767-786.
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    Clustering scientific documents with topic modeling. / Yau, C-K; Porter, A; Newman, N; Suominen, Arho (Corresponding Author).

    In: Scientometrics, Vol. 100, No. 3, 2014, p. 767-786.

    Research output: Contribution to journalArticleScientificpeer-review

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    AU - Porter, A

    AU - Newman, N

    AU - Suominen, Arho

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    KW - text analysis

    KW - latent dirichlet allocation

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