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The State of the Art in Sentiment Visualization
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-1907-7820
Lund University.ORCID iD: 0000-0002-7240-9003
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: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, no 1, p. 71-96, article id CGF13217Article in journal (Refereed) Published
Abstract [en]

Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational, and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer-reviewed publications together with an interactive web-based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data. 

Place, publisher, year, edition, pages
John Wiley & Sons, 2018. Vol. 37, no 1, p. 71-96, article id CGF13217
Keywords [en]
sentiment visualization, text visualization, sentiment analysis, opinion mining
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-62644DOI: 10.1111/cgf.13217ISI: 000426151300007OAI: oai:DiVA.org:lnu-62644DiVA, id: diva2:1091630
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2017-04-27 Created: 2017-04-27 Last updated: 2018-03-16Bibliographically approved

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Kucher, KostiantynKerren, Andreas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • 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