Skip to main content

Simple Approaches of Sentiment Analysis via Ensemble Learning

  • Conference paper
  • First Online:
Information Science and Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 339))

Abstract

Twitter has become a popular microblogging tool where users are increasing every minute. It allows its users to post messages of up to 140 characters each time; known as ‘Tweets’. Tweets have become extremely attractive to the marketing sector, since the user can either indicate customer success or presage public relations disasters far more quickly than web pages or traditional media. Moreover, the content of Tweets has become a current active research topic on sentiment polarity as positive or negative. Our experiment of sentiment analysis of contexts of tweets show that the accuracy performance can improve and be better achieved using ensemble learning, which is formed by the majority voting of the Support Vector Machine, Naive Bayes, SentiStrength and Stacking.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Shaikh MA, Prendinger H, Mitsuru I (2007) Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis. Paper presented at the Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction, Lisbon, Portugal,

    Google Scholar 

  2. Pang B, Lee L (2008) Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2 (1-2): 1-135

    Google Scholar 

  3. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Natural Language Processing, Project Report, Stanford:1-12

    Google Scholar 

  4. Gryc W, Moilanen K (2014) Leveraging Textual Sentiment Analysis with Social Network Modelling. From Text to Political Positions: Text analysis across disciplines 55:47

    Google Scholar 

  5. Tan S, Cheng X, Wang Y, Xu H (2009) Adapting naive bayes to domain adaptation for sentiment analysis. In: Advances in Information Retrieval. Springer, pp 337-349

    Google Scholar 

  6. Elangovan M, Ramachandran KI, Sugumaran V (2010) Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Systems with Applications 37 (3):2059-2065. doi:10.1016/j.eswa.2009.06.103

  7. Cufoglu A, Lohi M, Madani K (2008) Classification accuracy performance of Naive Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) - comparative study. Paper presented at the International Conference on Computer Engineering & Systems (ICCES),

    Google Scholar 

  8. Liu B (2007) Web data mining: exploring hyperlinks, contents, and usage data. Springer,

    Google Scholar 

  9. Liu B (2012) Sentiment Analysis and Opinion Mining. Morgan & Claypool,

    Google Scholar 

  10. Hope LR, Korb KB (2004) A bayesian metric for evaluating machine learning algorithms. Paper presented at the Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence, Cairns, Australia,

    Google Scholar 

  11. Tang H, Tan S, Cheng X (2009) A survey on sentiment detection of reviews. Expert Systems with Applications 36 (7):10760-10773

    Google Scholar 

  12. Kecman V (2005) Support Vector Machines – An Introduction. In: Wang L (ed) Support Vector Machines: theory and applications, vol 177. Springer Science & Business Media, pp 1-48

    Google Scholar 

  13. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) SentiStrength. University of Wolverhampton,

    Google Scholar 

  14. Thelwall M, Buckley K, Paltoglou G, Cai D (2010) Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61 (12):2544-2558

    Google Scholar 

  15. Krippendorff KH (1980) Content analysis: an introduction to its methodology. Calif Sage Publications, Beverly Hills, California

    Google Scholar 

  16. Polikar R (2012) Ensemble learning. In: Ensemble Machine Learning. Springer, pp 1-34

    Google Scholar 

  17. Wolpert DH (1992) Stacked generalization. Neural networks 5 (2):241-259

    Google Scholar 

  18. Wilson T, Kozareva Z, Nakov P, Ritter A, Rosenthal S, Stoyanov V SemEval- 2013 Task 2: Sentiment Analysis in Twitter. In: Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval), 2013. Association for Computational Linguistics,

    Google Scholar 

  19. Bird S, Klein E, Loper E (2009) Accessing Text Corpora and Lexical Resources. In: Natural Language Processing with Python. O’Reilly Media, p 60

    Google Scholar 

  20. Hu M, Liu B (2004) Mining and summarizing customer reviews. Paper presented at the Proceedings of the tenth ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) international conference on Knowledge discovery and data mining, Seattle, WA, USA,

    Google Scholar 

  21. Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y, Cardie C, Riloff E, Patwardhan S (2005) OpinionFinder: a system for subjectivity analysis. Paper presented at the Proceedings of HLT/EMNLP on Interactive Demonstrations, Vancouver, British Columbia, Canada,

    Google Scholar 

  22. Nielsen FÅ (2011) A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:11032903

    Google Scholar 

  23. Martin-Valdivia M-T, Martinez-Camara E, Perea-Ortega J-M, Urena-Lopez LA (2013) Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches. Expert Systems with Applications 40 (10):3934-3942

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tawunrat Chalothom .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chalothom, T., Ellman, J. (2015). Simple Approaches of Sentiment Analysis via Ensemble Learning. In: Kim, K. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46578-3_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46578-3_74

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46577-6

  • Online ISBN: 978-3-662-46578-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics