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Visualizing Analysis Results

  • Murugan Anandarajan
  • Chelsey Hill
  • Thomas Nolan
Chapter
Part of the Advances in Analytics and Data Science book series (AADS, volume 2)

Abstract

Text visualizations are the topic for this chapter. The chapter begins with general techniques to help create effective visualizations. From there, it moves to common visualizations used in text analysis. The chapter describes heat maps, word clouds, top term plots, cluster visualizations, topics over time, and network graphs.

Keywords

Text analytics Text visualization Word clouds Document visualization Document networks Text clouds 

References

  1. Alencar, A. B., de Oliveira, M. C. F., & Paulovich, F. V. (2012). Seeing beyond reading: A survey on visual text analytics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 476–492.Google Scholar
  2. Berinato, S. (2016). Good charts: The HBR guide to making smarter, more persuasive data visualizations. Cambridge, MA: Harvard Business Review Press.Google Scholar
  3. Data visualization: What it is and why it matters. SAS, SAS Institute, Inc. (2017, December 1). www.sas.com/en_us/insights/big-data/data-visualization.html
  4. Ellis, G., & Mansmann, F. (2010). Mastering the information age solving problems with visual analytics. In Eurographics (Vol. 2, p. 5).Google Scholar
  5. Gan, Q., Zhu, M., Li, M., Liang, T., Cao, Y., & Zhou, B. (2014). Document visualization: An overview of current research. Wiley Interdisciplinary Reviews: Computational Statistics, 6(1), 19–36.CrossRefGoogle Scholar
  6. Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014, January). Word cloud explorer: Text analytics based on word clouds. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 1833–1842). IEEE.Google Scholar
  7. Keim, D. A., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006, July). Challenges in visual data analysis. In Information Visualization. IV 2006. Tenth International Conference on (pp. 9–16). IEEE.Google Scholar
  8. Knaflic, C. N. (2015a). Storytelling with data: A data visualization guide for business professionals. Hoboken: John Wiley & Sons.CrossRefGoogle Scholar
  9. Knaflic, C. N. (2015b, May 13). Tell your audience what you want them to know. Storytelling with data. Retrieved April 14, 2018, from http://www.storytellingwithdata.com/blog/2015/05/tell-your-audience-what-you-want-them
  10. Knaflic, C. N. (2015c, June 03). Audience, audience, audience. Storytelling with data. Retrieved April 14, 2018, from http://www.storytellingwithdata.com/blog/2015/06/audience-audience-audience
  11. Knaflic, C. N. (2017, September 7). My guiding principles. Storytelling with data. Retrieved April 14, 2018, from http://www.storytellingwithdata.com/blog/2017/8/9/my-guiding-principles
  12. Kucher, K., & Kerren, A. (2015, April). Text visualization techniques: Taxonomy, visual survey, and community insights. In Visualization Symposium (PacificVis), 2015 IEEE Pacific (pp. 117–121). IEEE.Google Scholar
  13. Yang, Y., Akers, L., Klose, T., & Yang, C. B. (2008). Text mining and visualization tools–impressions of emerging capabilities. World Patent Information, 30(4), 280–293.CrossRefGoogle Scholar

Further Reading

  1. For an example of visualizing text analytics results in SAS Visual Text Analytics, see Chap. 16.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Murugan Anandarajan
    • 1
  • Chelsey Hill
    • 2
  • Thomas Nolan
    • 3
  1. 1.LeBow College of BusinessDrexel UniversityPhiladelphiaUSA
  2. 2.Feliciano School of BusinessMontclair State UniversityMontclairUSA
  3. 3.Mercury Data ScienceHoustonUSA

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