Automatic Computing of Global Emotional Polarity in French Health Forum Messages

  • Natalia GrabarEmail author
  • Loïc Dumonet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9105)


Social media provide the possibility for people to freely communicate. These discussion are rich with subjectivity and emotions, which is due to the anonymity of contributors. We propose to work on health fora in French and on subjective entities (e.g. emotions, feelings, uncertainties). Our specific interest is to study how the polarity of emotions is influenced by negation, uncertainty, modifiers and discoursive markers, and how the global polarity of sentences is constructed. We design a rule-based system and evaluate is against manually built reference data. Inter-annotator agreement is between 0.50 and 0.66. An evaluation of the automatic system shows between 40 and 56% precision.


Sentiment Analysis Semantic Annotation Discoursive Marker Discourse Connector Health Forum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akdag, H., DeGlas, M., Pacholczyk, D.: A qualitative theory of uncertainty. Fundamenta Informaticae 17(4), 333–362 (1992)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Augustyn, M., Ben Hamou, S., Bloquet, G., Goossens, V., Loiseau, M., Rynck, F.: Constitution de ressources pédagogiques numériques: le lexique des affects, pp. 407–414. Presses Universitaires de Grenoble (2008)Google Scholar
  3. 3.
    Bakliwal, A., Foster, J., van der Puil, J., O’Brien, R., Tounsi, L., Hughes, M.: Sentiment analysis of political tweets: towards an accurate classifier (2013)Google Scholar
  4. 4.
    Battaïa, C.: L’analyse de lémotion dans les forums de santé. JEP-TALN-RECITAL, RECITAL pp. 267–280 (2012)Google Scholar
  5. 5.
    Chauveau-Thoumelin, P., Grabar, N.: La subjectivité dans le discours médical: sur les traces de l’incertitude et des émotions. In: EGC 2014, pp. 455–466 (2014)Google Scholar
  6. 6.
    Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)CrossRefGoogle Scholar
  7. 7.
    Cornelis, C., DeCock, M., Kerre, E.: Efficient Approximate Reasoning with Positive and Negative Information, pp. 779–785 (2004)Google Scholar
  8. 8.
    Feng, S., Zhang, L., Li, B., Wang, D., Yu, G., Wong, K.F.: Is twitter a better corpus for measuring sentiment similarity? In: EMNLP, pp. 897–902 (2013)Google Scholar
  9. 9.
    Gauducheau, N.: La communication des émotions dans les échanges médiatisés par ordinateur: bilan et perspectives. Bulletin de Psychologie 61(4), 389–404 (2008)CrossRefGoogle Scholar
  10. 10.
    Huang, H., Yu, C., Lin, T., Chang, C., Chen, H.: Analyses of the association between discourse relation and sentiment polarity with a Chinese human-annotated corpus. LAW VII & ID, p. 70 (2013)Google Scholar
  11. 11.
    Huh, J., Yetisgen-Yildiz, M., Pratt, W.: Text classification for assisting moderators in online health communities. Journ. Biomed. Inform. 46(6), 998–1005 (2013)CrossRefGoogle Scholar
  12. 12.
    Landis, J., Koch, G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Li, S., Lee, S.Y.M., Chen, Y., Huang, C.R., Zhou, G.: Sentiment classification and polarity shifting. In: COLING, pp. 635–643 (2010)Google Scholar
  14. 14.
    Liu, Y., Yu, X., Liu, B., Chen, Z.: Sentence-level sentiment analysis in the presence of modalities. In: Gelbukh, A. (ed.) CICLing 2014, Part II. LNCS, vol. 8404, pp. 1–16. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  15. 15.
    Maurel, S., Curtoni, P., Dini, L.: L’analyse des sentiments dans les forums. Atelier Fouille des Données d’Opinion (2008)Google Scholar
  16. 16.
    Moreno-Ortiz, A., Pérez-Hernández, C., Del-Olmo, M., et al.: Managing multiword expressions in a lexicon-based sentiment analysis system for spanish. In: NAACL HLT 2013, vol. 13, p. 1 (2013)Google Scholar
  17. 17.
    Paroubek, P., Pak, A.: Le microblogage pour la microanalyse des sentiments et des opinions. TAL 51(3) (2010)Google Scholar
  18. 18.
    Ramteke, A., Malu, A., Bhattacharyya, P., Nath, J.S.: Detecting turnarounds in sentiment analysis: Thwarting. In: ACL, pp. 860–865 (2013)Google Scholar
  19. 19.
    Sayeed, A.B., Boyd-Graber, J.L., Rusk, B., Weinberg, A.: Grammatical structures for word-level sentiment detection. In: HLT-NAACL, pp. 667–676 (2012)Google Scholar
  20. 20.
    Tokuhisa, R., Inui, K., Matsumoto, Y.: Emotion classification using massive examples extracted from the web. In: COLING, pp. 881–888 (2008)Google Scholar
  21. 21.
    Zadeh, L.: A fuzzy-set-theoretic interpretation of linguistic hedges. Journal of Cybernetics 2(3), 4–34 (1972)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CNRS UMR 8163 STLUniversité Lille 3Villeneuve d’AscqFrance

Personalised recommendations