Skip to main content

A Text Mining Analysis on Big Data Extracted from Social Media

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12252))

Included in the following conference series:

  • 1509 Accesses

Abstract

The aim of this paper is to analyze data derived from Social Media. In our time people and devices constantly generate data. The network is generating location and other data that keeps services running and ready to use in every moment. This rapid development in the availability and access to data has induced the need for better analysis techniques to understand the various phenomena. We consider a Text Mining and a Sentiment Analysis of data extracted from Social Networks. The application regards a Text Mining Analysis and a Sentiment Analysis on Twitter, in particular on tweets regarding Coronavirus and SARS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bolasco, S.: Statistica testuale e Text Mining: Alcuni Paradigmi Applicativi, Quaderni di Statistica, vol. 7 (2005)

    Google Scholar 

  2. Branda, L.: Sindrome respiratoria acuta (SARS) (2018). https://www.msdmanuals.com/it-it/casa/infezioni/virus-respiratori/sindrome-respiratoria-acuta-grave-SARS. Accessed March 2020

  3. Inside Marketing pull information: Sentiment Analisis (2017). http://insidemarketing.it/glossario/definizione/sentiment-analysis/. Accessed March 2020

  4. Jan, A.K.: Data clustering. 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)

    Google Scholar 

  5. Govoni, L.: Text mining: il processo di estrazione del testo (2019). https://lorenzogovoni.com/text-mining/. Accessed March 2020. Hearst

  6. Laney, D.: 3D data management: controlling data volume, velocity and variety, application delivery strategies META GROUP (2001). https://blogs.gartner.com/. Accessed March 2020

  7. Lucarini, L.: (2020). https://giacomolucarini.it/sentiment-analysis-ci-aiuta-lintelligenza-artificiale/. Accessed March 2020

  8. Manhika, J., Chui, M., Brown, B., et al.: Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute (2011). www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_datathe_next_frontier. Accessed February 2018

  9. Mayer-Schonberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Mariner Books, Boston (2013)

    Google Scholar 

  10. Ministero della salute: “FAQ - Nuovo Coronavirus COVID-19” (2020). http://www.salute.gov.it/portale/nuovocoronavirus/dettaglioFaqNuovoCoronavirus.jsp?lingua=italiano&id=228. Accessed March 2020

  11. Piva, A.: Le 5 V dei Big Data: dal Volume al Valore (2019). https://blog.osservatori.net/it_it/le-5v-dei-big-data. Accessed March 2020

  12. Schoier, G., Borruso, G.: A methodology for dealing with spatial big data. Int. J. Bus. Intell. Data Mining 12(1), 1–13 (2017)

    Article  Google Scholar 

  13. Software Testing Help: Data Mining Vs Machine Learning Vs Artificial Intelligence Vs Deep Learning (2019). http://www.intelligenzaartificiale.it/data-mining/. Accessed March 2020

  14. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gabriella Schoier .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schoier, G., Borruso, G., Tossut, P. (2020). A Text Mining Analysis on Big Data Extracted from Social Media. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58811-3_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58810-6

  • Online ISBN: 978-3-030-58811-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics