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Term-community-based topic detection with variable resolution

Hamm, Andreas and Odrowski, Simon (2021) Term-community-based topic detection with variable resolution. [Other]

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Official URL: https://arxiv.org/abs/2103.13550


Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity. We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation. We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions. A comparison with topics detected by Latent Dirichlet Allocation is also included. Finally, we discuss factors that influence topic interpretation.

Item URL in elib:https://elib.dlr.de/141587/
Document Type:Other
Title:Term-community-based topic detection with variable resolution
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hamm, AndreasAndreas.Hamm (at) dlr.dehttps://orcid.org/0000-0001-5854-851X
Odrowski, SimonSimon.Odrowski (at) dlr.dehttps://orcid.org/0000-0001-9050-1711
Date:25 March 2021
Journal or Publication Title:arXiv.org
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:23
Keywords:Text mining, Natural language processing, Topic modeling, Term ranking, Community detection, Corpus analysis, Word embeddings
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment
Location: Köln-Porz
Institutes and Institutions:Think Tank
Deposited By: Hamm, Andreas
Deposited On:29 Mar 2021 12:39
Last Modified:29 Mar 2021 12:39

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