Skip to main content

Data Mining for Pulsing the Emotion on the Web

  • Protocol
  • First Online:
Data Mining in Clinical Medicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1246))

Abstract

The Internet is becoming an increasingly important part of our lives. Internet users share personal information and opinions on social media webs expressing their feelings, judgments, feelings or emotions easy. Text mining and information retrieval techniques allow us to explore all this information and discover what the authors’ opinions, claims, or assertions are. A general overview of sentiment analysis’ current approaches and its future challenges, providing basic information on their current trends, is made throughout this chapter.

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

Access this chapter

Protocol
USD 49.95
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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    http://stackoverflow.com/questions/4199441/best-algorithmic-approach-to-sentiment-analysis

References

  1. Parrott W (2001) Emotions in social psychology: essential readings. Psychology Press

    Google Scholar 

  2. Bo P, Lee L (2008) Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval. pp 1–135

    Google Scholar 

  3. Liu B (2012) Sentiment analysis and opinion mining. Morgan & Claypool, p 167

    Google Scholar 

  4. Mejova Y (2009) Sentiment analysis: an overview [Online]. [Cited: 08 08, 2013]. http://www.cs.uiowa. edu/~ymejova/publications/CompsYelenaMejova.pdf

    Google Scholar 

  5. Anthony A, Gamon M (2005) Customizing sentiment classifiers to new domains: a case study. Proceedings of the International Conference on Recent Advances in Natural Language Processing, vol 1. pp 2–1

    Google Scholar 

  6. Songbo T, Wu G, Tang H, Cheng X (2007) A novel scheme for domain‐transfer problem in the context of sentiment analysis. ACM proceedings of the sixteenth ACM conference on information and knowledge management. pp 979–982

    Google Scholar 

  7. Whitelaw C, Navendu G, Shlomo Argamon (2005) Using appraisal groups for sentiment analysis. Proceedings of the 14th ACM international conference on information and knowledge management. ACM. pp 625–631

    Google Scholar 

  8. Thelwall M, Kevan B (2013) Topic-based sentiment analysis for the social web: the role of mood and issue-related words. J Am Soc Inform Sci Technol 64:1608–1617. doi:10.1002/asi.22872

    Article  Google Scholar 

  9. He Y (2012) Incorporating sentiment prior knowledge for weakly supervised sentiment analysis. ACM Transactions on Asian Language Information Processing (TALIP) 11(2): 979–982

    Google Scholar 

  10. Alpaydin E (2004) Introduction to machine learning. MIT Press

    Google Scholar 

  11. Bo P, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. Association for Computational Linguistics. Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10. pp 79–86

    Google Scholar 

  12. Turney, PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 417–424

    Google Scholar 

  13. Maite T, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Computational linguistics. pp 267–307

    Google Scholar 

  14. Hurwitz R (2012) General Inquirer home page [Online]. [Cited: August 08, 2013]. http://www.wjh.harvard.edu/~inquirer/

  15. Pennebaker JW, Booth RJ, Francis ME (2007) Linguistic inquiry and word count [Online]. [Cited: August 08, 2013]. http://www.liwc.net/

  16. Princeton. Wordnet a lexical database for english [Online]. [Cited: Agosto 08, 2013]. http://wordnet.princeton.edu

  17. Andrea E, Fabrizio S (2010) SentiWordNet [Online]. [Cited: August 08, 2013]. http://sentiwordnet.isti.cnr.it/

  18. Whissell (2013) Emotional text recognition using Whissell’s dictionary of affective language [Online]. [Cited: August 08, 2013]. http://sail.usc.edu/dal_app.php

  19. MIT Media Laboratory (2013) Sentic API [Online]. [Cited: August 08, 2013]. http://sentic.net/api/

  20. Bo P, Lee L (2005) Movie review data [Online]. [Cited: August 08, 2013]. http://www.cs.cornell.edu/people/pabo/movie-review-data/

  21. Dredze M, Blitze J (2009) Multi-domain sentiment dataset [Online]. [Cited: August 08, 2013]. http://www.cs.jhu.edu/~mdredze/datasets/sentiment/

  22. Wilson T (2013) MPQA opinion corpus [Online]. [Cited: August 08, 2013]. mpqa.cs.pitt.edu

    Google Scholar 

  23. Google (2013) Google prediction API [Online]. [Cited: August 08, 2013]. https://developers.google.com/prediction/docs/sentiment_analysis

  24. Ling P (2005) Sentiment tutorial [Online]. [Cited: August 08, 2013]. http://alias-i.com/lingpipe/demos/tutorial/sentiment/read-me.html

  25. Kaplan AM, Michael H (2010) Users of the world, unite! the challenges and opportunities of social media. Business horizons, vol 1. pp 59–98

    Google Scholar 

  26. Duggan M, Brenner J (2013) The demographics of social media users — 2012. PewInternet [Online]. Pew Research centers. [Cited: August 08, 2013] http://pewinternet.org/Reports/2013/Social-media-users/The-State-of-Social-Media-Users.aspx

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jose Enrique Borras-Morell .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media, New York

About this protocol

Cite this protocol

Borras-Morell, J.E. (2015). Data Mining for Pulsing the Emotion on the Web. In: Fernández-Llatas, C., García-Gómez, J. (eds) Data Mining in Clinical Medicine. Methods in Molecular Biology, vol 1246. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1985-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-1985-7_8

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1984-0

  • Online ISBN: 978-1-4939-1985-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics