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Text Analysis to support structuring and modelling a public policy problem: outline of an algorithm to extract inferences from textual data
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: DSV Writers Hut: proceedings, Stockholm: Department of Computer and Systems Sciences, Stockholm University , 2014Conference paper, Published paper (Other academic)
Abstract [en]

Policy making situations are real-world problems that exhibit complexity in that they are composed of many interrelated problems and issues. To be effective, policies must holistically address the complexity of the situation rather than propose solutions to single problems. Formulating and understanding the situation and its complex dynamics, therefore, is a key to finding holistic solutions. Analysis of text based information on the policy problem, using Natural Language Processing (NLP) and Text analysis techniques, can support modelling of public policy problem situations in a more objective way based on domain experts’ knowledge and scientific evidence. The objective behind this study is to support modelling of public policy problem situations, using text analysis of verbal descriptions of the problem. We propose a formal methodology for analysis of qualitative data from multiple information sources on a policy problem to construct a causal diagram of the problem. The analysis process aims at identifying key variables, linking them by cause-effect relationships and mapping that structure into a graphical representation that is adequate for designing action alternatives, i.e., policy options. This study describes the outline of an algorithm used to automate the initial step of a larger methodological approach, which is so far done manually. In this initial step, inferences about key variables and their interrelationships are extracted from textual data to support a better problem structuring. A small prototype for this step is also presented.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University , 2014.
Series
DSV report series, ISSN 1101-8526 ; 14-019
Keyword [en]
Public policy, problem structuring, qualitative analysis, Natural Language Processing, algorithm, inference extraction
National Category
Information Systems
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-111107ISBN: 978-91-637-7457-7 (print)OAI: oai:DiVA.org:su-111107DiVA: diva2:774244
Conference
DSV writers hut 2014, August 21-22, Åkersberga, Sweden
Available from: 2014-12-22 Created: 2014-12-22 Last updated: 2016-10-26Bibliographically approved
In thesis
1.
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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
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  • nn-NO
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  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
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