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Enabling Reproducible Sentiment Analysis: A Hybrid Domain-Portable Framework for Sentiment Classification

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Book cover New Horizons in Design Science: Broadening the Research Agenda (DESRIST 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9073))

Abstract

In this paper a hybrid framework for Sentiment Analysis is presented. In the first part, dictionary based and machine learning based Sentiment Classification are introduced and the two approaches are contrasted. In the second part of the paper, the HSentiR framework, which combines the two approaches, is introduced. Consequently, the framework is evaluated regarding scoring accuracy and practical concerns.

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References

  1. Speed, J.G.: Do Newspapers now give the News? Forum Fam Plan West Hemisph 15, 704–711 (1893)

    Google Scholar 

  2. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)

    Article  Google Scholar 

  3. Muhammad, A., Wiratunga, N., Lothian, R., Glassey, R.: Domain-Based Lexicon Enhancement for Sentiment Analysis. In: BCS SGAI SMA 2013 BCS SGAI Work. Soc. Media Anal., pp. 7–18 (2013)

    Google Scholar 

  4. Aue, A., Gamon, M.: Customizing Sentiment Classifiers to New Domains: a Case Study. In: Proc. Recent Adv. Nat. Lang. Process RANLP, vol. 49, pp. 207–218 (2005), doi:10.1111/j.1745-3992.1984.tb00758.x

    Google Scholar 

  5. Prabowo, R., Thelwall, M.: Sentiment analysis: A combined approach. J. Informetr. 3, 143–157 (2009), doi: 10.1016/j.joi, 01.003

    Google Scholar 

  6. Gentleman, R., Temple Lang, D.: Statistical Analyses and Reproducible Research. J. Comput. Graph Stat. 16, 1–23 (2007), doi:10.1198/106186007X178663

    Article  MathSciNet  Google Scholar 

  7. Peng, R.D.: Reproducible Research in Computational Science. Science 334, 1226–1227 (2011), doi:10.1126/science.1213847

    Article  Google Scholar 

  8. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proc. 42nd Annu. Meet. Assoc. Comput. Linguist., p. 271 (2004)

    Google Scholar 

  9. Blair-Goldensohn, S., Hannan, K., McDonald, R., et al.: Building a sentiment summarizer for local service reviews. WWW Work. NLP Inf. Explos. Era (2008)

    Google Scholar 

  10. Arnold, I.J.M., Vrugt, E.B., Arnold Ivo, J.M., Vrugt Evert, B.: Fundamental uncertainty and stock market volatility. Appl. Financ. Econ. 18, 1425–1440 (2008), doi:10.1080/09603100701857922.

    Article  Google Scholar 

  11. Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia-Social Behav. Sci. 26, 55–62 (2011)

    Article  Google Scholar 

  12. Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22, 110–125 (2006)

    Article  MathSciNet  Google Scholar 

  13. Baron, D.P.: Competing for the public through the news media. J. Econ. Manag. Strateg. 14, 339–376 (2005), doi:10.1111/j.1530-9134.2005.00044.x

    Article  Google Scholar 

  14. Ludvigson, S.C.: Consumer confidence and consumer spending. J. Econ. Perspect. 18, 29–50 (2004)

    Article  Google Scholar 

  15. Steele, S.: Past and irrealis: just what does it all mean? Int. J. Am Linguist. 41, 200–217 (1975)

    Article  Google Scholar 

  16. Taboada, M., Brooke, J., Tofiloski, M., et al.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)

    Article  Google Scholar 

  17. Hatzivassiloglou, V., McKeown Kathleen, R.: Predicting the semantic orientation of adjectives. In: Proc. 35th Annu. Meet. Assoc. Comput. Linguist. Eighth Conf. Eur. Chapter Assoc. Comput. Linguist., pp. 174–181 (1997)

    Google Scholar 

  18. Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. 21, 315–346 (2003)

    Article  Google Scholar 

  19. Pang, B., Lee, L., Rd, H., et al.: Thumbs up?: sentiment classification using machine learning techniques. In: Proc. ACL 2002 Conf. Empir. Methods Nat. Lang. Process., vol. 10, pp. 79–86 (2002)

    Google Scholar 

  20. Salvetti, F., Reichenbach, C., Lewis, S.: Opinion polarity identification of movie reviews. In: Comput. Attitude Affect Text Theory Appl., pp. 303–316. Springer (2006)

    Google Scholar 

  21. Everitt, B.S.: The Cambridge Dictionary of Statistics, 2nd edn. Cambridge University Press, Cambridge (2002)

    MATH  Google Scholar 

  22. Held, L.: Methoden der statistischen Inferenz. Likelihood und Bayes. Heidelb. Spektrum Akad. Verl. (2008)

    Google Scholar 

  23. Mayo Deborah, G., Cox David, R.: Frequentist statistics as a theory of inductive inference. Lect. Notes-Monograph Ser. 77–97 (2006)

    Google Scholar 

  24. Read, J., Carroll, J.: Weakly supervised techniques for domain-independent sentiment classification. In: Proc. 1st Int. CIKM Work. Top. Anal. Mass Opin., pp. 45–52 (2009)

    Google Scholar 

  25. Li, S., Huang, C.-R., Zhou, G., Lee, S.Y.M.: Employing personal/impersonal views in supervised and semi-supervised sentiment classification. In: Proc. 48th Annu. Meet. Assoc. Comput. Linguist., pp. 414–423 (2010)

    Google Scholar 

  26. Berelson, B.: Content analysis in communication research. Society 44, 220 (1952), doi:10.1086/617924

    Google Scholar 

  27. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proc. Conf. Hum. Lang. Technol. Empir. Methods Nat. Lang. Process., pp. 347–354 (2005)

    Google Scholar 

  28. Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilive, D.M.: The General Inquirer. The M.I.T. Press, Cambridge (1966)

    Google Scholar 

  29. Feinerer, I., Hornik, K., Meyer, D.: Text Mining Infrastructure in R. J. Stat. Softw. 25, 1–54 (2008), doi: citeulike-article-id:2842334

    Google Scholar 

  30. Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66, 35–65 (2011)

    Google Scholar 

  31. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proc. tenth ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 168–177 (2004)

    Google Scholar 

  32. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38, 39–41 (1995)

    Article  Google Scholar 

  33. Baccianella, S., Esuli, A., Sebastiani, F.: SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In: Proc. Lr. Seventh Int. Conf. Lang. Resour. Eval., pp. 2200–2204 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Georg-August University, Göttingen, Germany

    Matthias Eickhoff

Authors
  1. Matthias Eickhoff
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Corresponding author

Correspondence to Matthias Eickhoff .

Editor information

Editors and Affiliations

  1. Maynooth University, Maynooth, Ireland

    Brian Donnellan

  2. Dublin City University, Dublin, Ireland

    Markus Helfert

  3. Intel Labs Europe, Leixlip, Ireland

    Jim Kenneally

  4. Florida International University, Miami, Florida, USA

    Debra VanderMeer

  5. University of Nevada, Las Vegas, Nevada, USA

    Marcus Rothenberger

  6. Universität St. Gallen, St. Gallen, Switzerland

    Robert Winter

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© 2015 Springer International Publishing Switzerland

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Eickhoff, M. (2015). Enabling Reproducible Sentiment Analysis: A Hybrid Domain-Portable Framework for Sentiment Classification. In: Donnellan, B., Helfert, M., Kenneally, J., VanderMeer, D., Rothenberger, M., Winter, R. (eds) New Horizons in Design Science: Broadening the Research Agenda. DESRIST 2015. Lecture Notes in Computer Science(), vol 9073. Springer, Cham. https://doi.org/10.1007/978-3-319-18714-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-18714-3_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18713-6

  • Online ISBN: 978-3-319-18714-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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