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Text mining and visualization tools - Impressions of emerging capabilities

Author

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  • Yang, YunYun
  • Akers, Lucy
  • Klose, Thomas
  • Barcelon Yang, Cynthia

Abstract

Innovation is the underlying foundation of today's competitive economy and technological advancement. There is a plethora of text mining and visualization tools available on the market to facilitate the innovative process in uncovering "hidden nuggets" of information about emerging technologies. A high-level overview of some key text mining and visualization tools is presented in this paper to provide a comparison of text mining capabilities, perceived strengths, potential limitations, applicable data sources, and output of results, as applied to chemical, biological and patent information. Examples of tools to be discussed include sophisticated text mining software packages, some simpler full-text searching tools, and a few data visualization tools that could be integrated with the more sophisticated software packages and full-text searching tools. Included are comments on our impressions of applicability of these tools to different types of data sources, perceived strengths, potential limitations, and suggestions as to which user groups may benefit from these tools.

Suggested Citation

  • Yang, YunYun & Akers, Lucy & Klose, Thomas & Barcelon Yang, Cynthia, 2008. "Text mining and visualization tools - Impressions of emerging capabilities," World Patent Information, Elsevier, vol. 30(4), pages 280-293, December.
    • Handle: RePEc:eee:worpat:v:30:y:2008:i:4:p:280-293
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    Citations

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    Cited by:

    1. Martin G. Moehrle & Jan M. Gerken, 2012. "Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 805-826, June.
    2. Lothar Walter & Alfred Radauer & Martin G. Moehrle, 2017. "The beauty of brimstone butterfly: novelty of patents identified by near environment analysis based on text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 103-115, April.
    3. Martin G. Moehrle, 2010. "Measures for textual patent similarities: a guided way to select appropriate approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 95-109, October.
    4. Lybbert, Travis J. & Zolas, Nikolas J., 2014. "Getting patents and economic data to speak to each other: An ‘Algorithmic Links with Probabilities’ approach for joint analyses of patenting and economic activity," Research Policy, Elsevier, vol. 43(3), pages 530-542.
    5. Jason Jihoon Ree & Cheolhyun Jeong & Hyunseok Park & Kwangsoo Kim, 2019. "Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    6. Guifeng Liu, 2013. "Visualization of patents and papers in terahertz technology: a comparative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1037-1056, March.

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