Skip to main content

Sentiment Analysis

  • 374 Accesses

Abstract

Sentiment or opinion analysis employs natural language processing to extract a significant pattern of knowledge from a large amount of textual data. It examines comments, opinions, emotions, beliefs, views, questions, preferences, attitudes, and requests communicated by the writer in a string of text. It extracts the writer’s feelings in the form of subjectivity (objective and subjective), polarity (negative, positive, and neutral), and emotions (angry, happy, surprised, sad, jealous, and mixed). Thus, this chapter covers the theoretical framework and use cases of sentiment analysis in libraries. The chapter is followed by a case study showing the application of sentiment analysis in libraries using two different tools.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-85085-2_7
  • Chapter length: 21 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)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Palmer S (2014) Characterizing university library use of social media a case study of Twitter and Facebook from Australia. J Acad Librarianship 40(6):611–619. https://doi.org/10.1016/j.acalib.2014.08.007

    CrossRef  Google Scholar 

  2. Burkhardt A (2010) Social media: a guide for college and university libraries. College Res Libraries News 71(1):10–24. https://crln.acrl.org/index.php/crlnews/article/view/8302

    CrossRef  Google Scholar 

  3. Collins G, Haines L (2012) Measuring libraries’ use of YouTube as a promotional tool: an exploratory study and proposed best practices. J Web Librarianship 6(1):5–31. http://dx.doi.org/10.1080/19322909.2012.641789

    CrossRef  Google Scholar 

  4. Aziz NA, Chia YB, Loh H (2010) Sowing the seeds: towards reaping a harvest using social web applications in Nanyang Technological University Library. In: World library and information congress: 76th IFLA general conference and assembly, Gothenburg. https://www.ifla.org/past-wlic/2010/245-aziz-en.pdf. Accessed 24 May 2021

  5. Aharony N (2012) Facebook use in libraries: an exploratory analysis. Aslib Proc 64:358–372. https://doi.org/10.1108/00012531211244725

    CrossRef  Google Scholar 

  6. Kim Y, Abbas J (2010) Adoption of Library 2.0 functionalities by academic libraries and users: a knowledge management perspective. J Acad Librarianship 36(3):211–218. http://dx.doi.org/10.1016/j.acalib.2010.03.003

    CrossRef  Google Scholar 

  7. Landis C (2007) Social networking sites: getting friendly with our users. College Res Libraries News 68(11):709–712. https://doi.org/10.5860/crln.68.11.7907

    CrossRef  Google Scholar 

  8. Matthews B (2006) Do you Facebook: networking with students online. College Res Libraries News 67(5):306–307. https://doi.org/10.5860/crln.67.5.7622

    CrossRef  Google Scholar 

  9. Miller SE, Jensen LA (2007) Connecting and communicating with students on Facebook. Comput Libraries 27(8):18–22

    Google Scholar 

  10. Mack D, Behler A, Roberts B, Rimland E (2007) Reaching students with Facebook: data and best practices. Electron J Acad Special Librarianship 8(2). https://digitalcommons.unl.edu/ejasljournal/85/

  11. Chen DY-T, Chu SK-W, Xu S-Q (2012) How do libraries use social networking sites to interact with users. Proc Amer Soc Inf Sci Technol 49:1–10. https://doi.org/10.1002/meet.14504901085

    Google Scholar 

  12. Salisbury L, Laincz J, Smith J (2012) Science and Technology Undergraduate Students’ Use of the Internet, Cell Phones and Social Networking Sites to Access Library Information. University Libraries Faculty Publications and Presentations. https://scholarworks.uark.edu/libpub/10. Accessed 24 May 2021

    Google Scholar 

  13. Yep J, Brown M, Fagliarone G, Shulman J (2017) Influential players in Twitter networks of libraries at primarily undergraduate institutions. J Acad Librarianship 43:193–200. https://doi.org/10.1016/j.acalib.2017.03.005

    CrossRef  Google Scholar 

  14. Lund BD (2020) Assessing library topics using sentiment analysis in R: a discussion and code sample. Public Serv Quarterly 16(2):112–123. https://doi.org/10.1080/15228959.2020.1731402

    CrossRef  Google Scholar 

  15. Patra SK (2019) How Indian libraries tweet? Word frequency and sentiment analysis of library tweets. Ann Library Inf Stud 66(4):131–139. http://op.niscair.res.in/index.php/ALIS/article/view/26636/465477307

    Google Scholar 

  16. Al-Daihani SM, Abrahams A (2016) A text mining analysis of academic libraries’ Tweets. J Acad Librarianship 42(2):135–143. https://doi.org/10.1016/j.acalib.2015.12.014

    CrossRef  Google Scholar 

  17. Stewart B, Walker J (2018) Build it and they will come? Patron engagement via Twitter at historically black college and university libraries. J Acad Librarianship 44:118–124. https://doi.org/10.1016/j.acalib.2017.09.016

    CrossRef  Google Scholar 

  18. Al-Daihani SM, Abrahams A (2018) Analysis of academic libraries’ Facebook posts: text and data analytics. J Acad Librarianship 44:216–225. https://doi.org/10.1016/j.acalib.2018.02.004

    CrossRef  Google Scholar 

  19. Lamba M, Madhusudhan M (2018) Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity. Soc Netw Anal Min 8:63. https://doi.org/10.1007/s13278-018-0541-y

    CrossRef  Google Scholar 

  20. Logan J, Barrett K, Pagotto S (2019) Dissatisfaction in chat reference users: a transcript analysis study. College Res Libraries 80:925. https://doi.org/10.5860/crl.80.7.925

    CrossRef  Google Scholar 

  21. Friedrich N, Bowman TD, Stock WG, Haustein S (2015) Adapting sentiment analysis for tweets linking to scientific papers. In: 15th international society of scientometrics and informetrics conference (ISSI 2015), Istanbul. https://arxiv.org/abs/1507.01967. Accessed 21 May 2021

  22. Hassan S-U, Saleem A, Soroya SH, et al (2020) Sentiment analysis of tweets through altmetrics: a machine learning approach. J Inf Sci 0165551520930917. https://doi.org/10.1177/0165551520930917

  23. Hassan S-U, Aljohani NR, Tarar UI, et al (2020) Exploiting Tweet Sentiments in Altmetrics Large-Scale Data. https://arxiv.org/abs/2008.13023. Accessed 21 May 2021

Download references

Author information

Authors and Affiliations

Authors

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). Sentiment Analysis. In: Text Mining for Information Professionals. Springer, Cham. https://doi.org/10.1007/978-3-030-85085-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85085-2_7

  • 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)