Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions
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| Titel: | Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions |
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| Autoren: | Cardiff, John, Skitalinskaya, Gabriella, Alexandrov, Mikhail |
| Quelle: | Conference Papers |
| Verlagsinformationen: | Technological University Dublin |
| Publikationsjahr: | 2017 |
| Bestand: | Dublin Institute of Technology: ARROW@DIT (Archiving Research Resources on he Web) |
| Schlagwörter: | Dynamic Topic Modeling, Matrix Decomposition, Latent Dirichlet Allocation, Computer Sciences, Numerical Analysis and Scientific Computing |
| Beschreibung: | In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve results comparable to those obtained using complex probabilistic models, such as LDA. |
| Publikationsart: | conference object |
| Dateibeschreibung: | application/pdf |
| Sprache: | unknown |
| Relation: | https://arrow.tudublin.ie/ittscicon/11; https://arrow.tudublin.ie/context/ittscicon/article/1011/viewcontent/2017_Skitalinskaya_et_al_Comparison_of_two_pass_algorithms.pdf |
| DOI: | 10.1007/978-3-030-02840-4_3 |
| Verfügbarkeit: | https://arrow.tudublin.ie/ittscicon/11 https://doi.org/10.1007/978-3-030-02840-4_3 https://arrow.tudublin.ie/context/ittscicon/article/1011/viewcontent/2017_Skitalinskaya_et_al_Comparison_of_two_pass_algorithms.pdf |
| Rights: | Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence ; http://creativecommons.org/licenses/by/4.0/ |
| Dokumentencode: | edsbas.7ACE3C23 |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve results comparable to those obtained using complex probabilistic models, such as LDA. |
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| DOI: | 10.1007/978-3-030-02840-4_3 |