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Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Potsdam University, Germany. (ISOVIS)ORCID iD: 0000-0001-6164-7762
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-1907-7820
Potsdam University, Germany.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2018 (English)In: Proceedings of the LREC 2018 Workshop “The 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III)” / [ed] Mennatallah El-Assady, Annette Hautli-Janisz, and Verena Lyding, Paris, France: European Language Resources Association (ELRA) , 2018, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

The large collections of opinionated text that are continuously being created online, e.g., in the form of forum posts or tweets, contain arguments that might help us to better understand why opinions are held. While the task of manually extracting arguments from these large collections is an intractable one, a tool for computer-assisted extraction can (i) automatically select a subset of the text collection that contains re-occurring arguments to minimise the amount of text that the human coder has to read, and (ii) present the selected texts in a way that facilitates manual coding of arguments. We propose a tool called Topics2Themes that uses topic modelling to automatically extract important topics as well as the terms and texts most closely associated with each topic. We also provide a graphical user interface for manual argument coding, in which the user can search for arguments in the texts selected, create a theme for each type of argument detected and connect it to the texts in which it is found. Topics, terms, texts and themes are displayed as elements in four separate lists, and associations between the elements are visualised through connecting links. It is also possible to focus on one particular element through the sorting functionality provided, e.g., when a topic is selected, the terms, texts and themes associated with this topic are sorted as the top-ranked elements in their respective lists. The text collection can thereby be explored from different angles, which can be used to facilitate the argument coding and gain an overview and understanding of the arguments found in the texts. 

Place, publisher, year, edition, pages
Paris, France: European Language Resources Association (ELRA) , 2018. p. 9-16
Keywords [en]
argument extraction, topic modelling, text analysis, argument visualization, stance visualization, text visualization, information visualization, interaction
National Category
Language Technology (Computational Linguistics) Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-70911ISBN: 979-10-95546-13-9 (print)OAI: oai:DiVA.org:lnu-70911DiVA, id: diva2:1182842
Conference
3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III) at LREC '18, 12 May, 2018, Miyazaki, Japan
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Swedish Research Council, 2016-06681Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-05-24

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Skeppstedt, MariaKucher, KostiantynKerren, Andreas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • Other locale
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Output format
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