Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions

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Bibliographic Details
Title: Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions
Authors: Cardiff, John, Skitalinskaya, Gabriella, Alexandrov, Mikhail
Source: Conference Papers
Publisher Information: Technological University Dublin
Publication Year: 2017
Collection: Dublin Institute of Technology: ARROW@DIT (Archiving Research Resources on he Web)
Subject Terms: Dynamic Topic Modeling, Matrix Decomposition, Latent Dirichlet Allocation, Computer Sciences, Numerical Analysis and Scientific Computing
Description: 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.
Document Type: conference object
File Description: application/pdf
Language: 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
Availability: 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/
Accession Number: edsbas.7ACE3C23
Database: BASE
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