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Computational Analyses of Scientific Publications Using Raw and Manually Curated Data with Applications to Text Visualization
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Text visualization is a field dedicated to the visual representation of textual data by using computer technology. A large number of visualization techniques are available, and now it is becoming harder for researchers and practitioners to choose an optimal technique for a particular task among the existing techniques. To overcome this problem, the ISOVIS Group developed an interactive survey browser for text visualization techniques. ISOVIS researchers gathered papers which describe text visualization techniques or tools and categorized them according to a taxonomy. Several categories were manually assigned to each visualization technique. In this thesis, we aim to analyze the dataset of this browser. We carried out several analyses to find temporal trends and correlations of the categories present in the browser dataset. In addition, a comparison of these categories with a computational approach has been made. Our results show that some categories became more popular than before whereas others have declined in popularity. The cases of positive and negative correlation between various categories have been found and analyzed. Comparison between manually labeled datasets and results of computational text analyses were presented to the experts with an opportunity to refine the dataset. Data which is analyzed in this thesis project is specific to text visualization field, however, methods that are used in the analyses can be generalized for applications to other datasets of scientific literature surveys or, more generally, other manually curated collections of textual documents.

Place, publisher, year, edition, pages
2018. , p. 67
Keywords [en]
Scientific literature analysis, meta-analysis, trends, correlation, NLP, text mining, topic modeling, LDA, HDP, text visualization
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-78995OAI: oai:DiVA.org:lnu-78995DiVA, id: diva2:1266673
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 120 credits
Supervisors
Examiners
Available from: 2018-12-03 Created: 2018-11-29 Last updated: 2018-12-03Bibliographically approved

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3233343536373835 of 83
CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf