A Fuzzy Linguistics Supported Model to Measure the Contextual Bias in Sentiment Polarity

  • Juan Bernabé-Moreno
  • Alvaro Tejeda-Lorente
  • Carlos Porcel
  • Enrique Herrera-ViedmaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 641)


The polarity detection problem typically relies on experimental dictionaries, where terms are assigned polarity scores lacking contextual information. As a matter of fact, the polarity is highly dependant on the domain or community it is analysed, so we can speak of a contextual bias. We propose a method supported by fuzzy linguistic modelling to quantify this contextual bias and to enable the bias-aware sentiment analysis. To show how our approach work, we measure the bias of common concepts in two different domains and discuss the results.


Sentiment analysis Polarity Linguistic modelling Fuzzy logic Contextual bias 



This paper has been developed with the FEDER financing under Projects TIN2013-40658-P and TIN2016-75850-R.


  1. 1.
    Araújo, M., Gonçalves, P., Cha, M., Benevenuto, F.: iFeel: a system that compares and combines sentiment analysis methods. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 75–78. ACM (2014)Google Scholar
  2. 2.
    Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)Google Scholar
  3. 3.
    Bernabé-Moreno, J., Tejeda-Lorente, A., Porcel, C., Fujita, H., Herrera-Viedma, E.: Caresome: a system to enrich marketing customers acquisition and retention campaigns using social media information. Knowl. Based Syst. 80, 163–179 (2015)CrossRefGoogle Scholar
  4. 4.
    Bernabé-Moreno, J., Tejeda-Lorente, A., Porcel, C., Fujita, H., Herrera-Viedma, E.: Emotional profiling of locations based on social media. Procedia Comput. Sci. 55, 960–969 (2015)CrossRefGoogle Scholar
  5. 5.
    Cambria, E., Poria, S., Bajpai, R., Schuller, B.: Senticnet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: the 26th International Conference on Computational Linguistics (COLING), Osaka (2016)Google Scholar
  6. 6.
    Daku, M., Soroka, S., Young, L.: Lexicoder, version 2.0 (software). McGill University, Montreal (2011)Google Scholar
  7. 7.
    Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2010)CrossRefGoogle Scholar
  8. 8.
    Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 27–38. ACM (2013)Google Scholar
  9. 9.
    Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., Donaldson, L.: Use of sentiment analysis for capturing patient experience from free-text comments posted online. J. Med. Internet Res. 15(11), e239 (2013)CrossRefGoogle Scholar
  10. 10.
    Herrera, F., Herrera-Viedma, E.: Aggregation operators for linguistic weighted information. IEEE Trans. Syst. Man Cybern. Part A Syst. 27, 646–656 (1997)CrossRefGoogle Scholar
  11. 11.
    Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)CrossRefGoogle Scholar
  12. 12.
    Herrera, F., Martínez, L.: A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31(2), 227–234 (2001)CrossRefGoogle Scholar
  13. 13.
    Herrera, F., Herrera-Viedma, E., Alonso, S., Chiclana, F.: Computing with words and decision making. Fuzzy Optim. Decis. Mak. 8(4), 323–324 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Hu, M., Liu, B.: Mining opinion features in customer reviews. AAAI 4, 755–760 (2004)Google Scholar
  15. 15.
    Ieong, S., Mishra, N., Sadikov, E., Zhang, L.: Domain bias in web search. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 413–422. ACM (2012)Google Scholar
  16. 16.
    Iqbal, M., Karim, A., Kamiran, F.: Bias-aware lexicon-based sentiment analysis. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 845–850. ACM (2015)Google Scholar
  17. 17.
    Jockers, M.L.: Revealing Sentiment and Plot Arcs with the Syuzhet Package, February 2015Google Scholar
  18. 18.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count: LIWC 2001, vol. 71. Lawrence Erlbaum Associates, Mahway (2001)Google Scholar
  19. 19.
    Polanyi, L., Zaenen, A.: Contextual valence shifters. In: Computing Attitude and Affect in Text: Theory and Applications, pp. 1–10. Springer, Dordrecht (2006)Google Scholar
  20. 20.
    Salter-Townshend, M., Murphy, T.B.: Mixtures of biased sentiment analysers. Adv. Data Anal. Classif. 8(1), 85–103 (2014)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Schmid, H.: Improvements in part-of-speech tagging with an application to German. In: Proceedings of the ACL SIGDAT-Workshop. Citeseer (1995)Google Scholar
  22. 22.
    Thelwall, M.: Heart and soul: sentiment strength detection in the social web with sentistrength. In: Proceedings of the CyberEmotions, pp. 1–14 (2013)Google Scholar
  23. 23.
    Wadbude, R., Gupta, V., Mekala, D., Jindal, J., Karnick, H.: User bias removal in fine grained sentiment analysis. arXiv preprint arXiv:1612.06821 (2016)
  24. 24.
    Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 US presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics (2012)Google Scholar
  25. 25.
    Watson, D., Clark, L.A., Tellegen, A.: Development and validation of brief measures of positive and negative affect: the panas scales. J. Pers. Soc. Psychol. 54(6), 1063 (1988)CrossRefGoogle Scholar
  26. 26.
    West, R., Paskov, H.S., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. arXiv preprint arXiv:1409.2450 (2014)
  27. 27.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)CrossRefGoogle Scholar
  28. 28.
    Zadeh, L.: The concept of a linguistic variable and its applications to approximate reasoning. Part I Inf. Sci. 8, 199–249 (1975). Part II Inf. Sci. 8, 301–357 (1975), Part III Inf. Sci. 9, 43–80 (1975)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Juan Bernabé-Moreno
    • 1
  • Alvaro Tejeda-Lorente
    • 1
  • Carlos Porcel
    • 2
  • Enrique Herrera-Viedma
    • 1
    Email author
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversity of JaénJaénSpain

Personalised recommendations