Skip to main content
Book cover

Text Mining for Information Professionals pp 1–31Cite as

The Computational Library

  • 362 Accesses

Abstract

This chapter introduces the concept of computational research and thinking in libraries. It covers (i) the basic concept of text mining, (ii) the need for text mining in libraries, (iii) understanding text characteristics, and (iv) identifying different text mining problems, which include document classification/text categorization, information retrieval, clustering, and information extraction. It presents various use cases of libraries that have applied text mining techniques and enumerates various costs, limitations, and benefits related to text mining. This chapter is followed by a case study showing the clustering of documents using two different tools.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-85085-2_1
  • Chapter length: 31 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-85085-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Fay B (2019) The computational library. https://libraries.mit.edu/news/bibliotech/computational-library/. Accessed 21 June 2021

  2. Wing J (2006) Computational thinking. Commun ACM 49(3):33–35

    CrossRef  Google Scholar 

  3. ALA (2018) What & why of CT. Libraries ready to code https://www.ala.org/tools/readytocode/what-why-of-ct. Accessed 22 June 2021

  4. ALA (2018) Libraries ready to code Infographic. https://www.ala.org/advocacy/sites/ala.org.advocacy/files/content/pdfs/draft%20v6%20-%20LRTC_infographic%20(2).pdf. Accessed 25 June 2021

  5. Luhn H (1958) The automatic creation of literature abstracts. IBM J Res Dev 2(2):159–165

    MathSciNet  CrossRef  Google Scholar 

  6. Miner G, Elder J, Fast A, Hill T, Nisbet R, Delen D (2012) Practical text mining and statistical analysis for non-structured text data applications. Academic Press. https://www.oreilly.com/library/view/practical-text-mining/9780123869791/. Accessed 22 June 2021

    Google Scholar 

  7. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423. https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf. Accessed 22 June 2021

    MathSciNet  CrossRef  Google Scholar 

  8. Bates M (1995) Models of natural language understanding. PNAS 92:9977. https://doi.org/10.1073/pnas.92.22.9977

    CrossRef  Google Scholar 

  9. Cutting DR, Karger DR, Pederson JO, Tukey JW (1992) Scatter/Gather: a cluster-based approach in browsing large document collections. In: Proc 15th annual international ACM-SIGIR conference of research and development in information retrieval, Copenhagen. ACM Press, New York, pp 318–329

    Google Scholar 

  10. Tombros Villa R, Rijsbergen CJ (2002) The effectiveness of query-specific hierarchic clustering in information retrieval. Inf Process Manag 38(4):559–582

    CrossRef  Google Scholar 

  11. Salton G, Allan J, Buckley C, Singhal A (1996) Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts. In: Agosti M, Smeaton AF (eds) Information retrieval and hypertext. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1373-1_3

    Google Scholar 

  12. Anoopa VS, Asharafa S, Deepak P (2016) Unsupervised concept hierarchy learning: A topic modeling guided approach. Procedia Comput Sci 89:386–394. https://doi.org/10.1016/j.procs.2016.06.086

    CrossRef  Google Scholar 

  13. Ranganathan SR (1931) The five laws of library science. Madras Library Association, Madras

    Google Scholar 

  14. Nicholson S (2003) The bibliomining process: Data warehousing and data mining for library decision making. Inf Technol Libr 22(4):146–150

    Google Scholar 

  15. Lammey R (2015) CrossRef text and data mining services. Insights 28:62–68. https://doi.org/10.1629/uksg.233

    CrossRef  Google Scholar 

  16. Lamba M, Madhusdhuan M (2018) Application of topic mining and prediction modeling tools for library and information science journals. In: Library practices in digital era: Festschrift in Honor of Prof. V Vishwa Mohan. BS Publications, Hyderabad, pp 395–401. https://doi.org/10.5281/zenodo.1298739

    Google Scholar 

  17. Salloum SA, Al-Emran M, Monem AA, Shaalan K (2018) Using text mining techniques for extracting information from research articles. In: Shaalan K, Hassanien AE, Tolba F (eds) Intelligent natural language processing: Trends and applications. Springer International Publishing, Cham, pp 373–397

    CrossRef  Google Scholar 

  18. Reimer T (2012) Key issue: Text mining, copyright and the benefits and barriers to innovation. Insights 25:212–215. https://doi.org/10.1629/2048-7754.25.2.212

    CrossRef  Google Scholar 

  19. Sinoara RA, Antunes J, Rezende SO (2017) Text mining and semantics: a systematic mapping study. J Braz Comput Soc 23:9. https://doi.org/10.1186/s13173-017-0058-7

    CrossRef  Google Scholar 

  20. Weiss SM, Indurkhya N, Zhang T, Damerau FJ (2005) Overview of text mining. In: Weiss SM, Indurkhya N, Zhang T, Damerau FJ (eds) Text mining: Predictive methods for analyzing unstructured information. Springer, New York, NY, pp 1–13

    CrossRef  Google Scholar 

  21. Allahyari M, Pouriyeh S, Assefi M, et al (2017) A brief survey of text mining: Classification, clustering and extraction techniques. https://arxiv.org/pdf/1707.02919.pdf. Accessed 23 June 2021

  22. Savoy J (2018) Working with text. Tools, techniques and approaches for text mining. In: Tonkin EL, Tourte GJL (eds). Chandos Publisher, Cambridge, MA, 2016, 330 pp. (ISBN 978-1-84334-749-1). J Assoc Inf Sci Tech 69:181–184. https://doi.org/10.1002/asi.23899

    CrossRef  Google Scholar 

  23. Jones TW (2019) Document clustering. https://cran.ms.unimelb.edu.au/web/packages/textmineR/vignettes/b_document_clustering.html. Accessed 22 June 2021

Download references

Author information

Authors and Affiliations

  1. Library and Information Science, University of Delhi, Delhi, India

    Manika Lamba & Margam Madhusudhan

Authors
  1. Manika Lamba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Margam Madhusudhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Lamba, M., Madhusudhan, M. (2022). The Computational Library. In: Text Mining for Information Professionals. Springer, Cham. https://doi.org/10.1007/978-3-030-85085-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85085-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85084-5

  • Online ISBN: 978-3-030-85085-2

  • eBook Packages: Computer ScienceComputer Science (R0)