Sentiment Analysis of News Titles

The Role of Entities and a New Affective Lexicon
  • Daniel Loureiro
  • Goreti Marreiros
  • José Neves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7026)


The growth of content on the web has been followed by increasing interest in opinion mining. This field of research relies on accurate recognition of emotion from textual data. There’s been much research in sentiment analysis lately, but it always focuses on the same elements. Sentiment analysis traditionally depends on linguistic corpora, or common sense knowledge bases, to provide extra dimensions of information to the text being analyzed. Previous research hasn’t yet explored a fully automatic method to evaluate how events associated to certain entities may impact each individual’s sentiment perception. This project presents a method to assign valence ratings to entities, using information from their Wikipedia page, and considering user preferences gathered from the user’s Facebook profile. Furthermore, a new affective lexicon is compiled entirely from existing corpora, without any intervention from the coders.


Opinion Mining Sentiment Analysis Emotion Category Word Sense Disambiguation Prepositional Phrase 
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]
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia A Crystallization Point for the Web of Data. Journal of Web Semantics: Science, Services and Agents on the World Wide Web (7), 154–165 (2009)Google Scholar
  2. [2]
    Bradley, M.M., Lang, P.J., Cuthbert, B.N.: Affective Norms for English Words in Center for the Study of Emotion and Attention National Institute of Mental Health, University of Florida (1997)Google Scholar
  3. [3]
    Chaumartin, F.: UPAR7: A knowledge-based system for headline sentiment tagging. In: SemEval 2007, Prague, ACL, pp. 422–425 (2007)Google Scholar
  4. [4]
    Ekman, P.: Facial expression of emotion. American Psychologist 48, 384–392 (1993)CrossRefGoogle Scholar
  5. [5]
    Esuli, A., Sebastiani, F.: SentiWordnet: A Publicly Available Lexical in Resource for Opinion Mining. In: LREC 2006 (2006)Google Scholar
  6. [6]
    Fellbaum, C.: WordNet: An Electronic Lexical Databases. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  7. [7]
    Finkel, J., Grenager, T., Manning, C.: Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In: Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363–370 (2005)Google Scholar
  8. [8]
    Goldberg, R.: The structure of phenotypic personality traits. American Psychologist 48(1), 26–34 (1993)CrossRefGoogle Scholar
  9. [9]
    Liu, H., Lieberman, H., Selker, T.: A Model of Textual Affect Sensing using Real-World Knowledge. In: Proceedings of the 2003 International Conference on Intelligent User Interfaces, pp. 125–132 (2003)Google Scholar
  10. [10]
    Liu, H., Singh, P.: ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal 22(4), 211–226 (2004)CrossRefGoogle Scholar
  11. [11]
    Liu, H., Maes, P.: Interestmap: Harvesting social network profiles for recommendations in Beyond Personalization - IUI (2005)Google Scholar
  12. [12]
    Loureiro, D.: Facebook’s hidden feature: User Models and InterestMaps. In: 4th Meeting of Young Researchers, UP, IJUP (2011)Google Scholar
  13. [13]
    Marneffe, M., Manning, C.: Stanford typed dependencies manual (2008)Google Scholar
  14. [14]
    Marneffe, M., MacCartney, B., Manning, C.D.: Generating Typed Dependency Parses from Phrase Structure Parses in LREC (2006)Google Scholar
  15. [15]
    Mihalcea, R., Liu, H.: A corpus-based approach to finding happiness. In: The AAAI Spring Symposium on Computational Approaches to Weblogs (2006)Google Scholar
  16. [16]
    Minsky, M.: The Emotion Machine. Simon & Schuster, New York (2006)Google Scholar
  17. [17]
    Vinagre, E., Marreiros, G., Ramos, C., Figueiredo, L.: An Emotional and Context-Aware Model for Adapting RSS News to Users and Groups. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS, vol. 5816, pp. 187–198. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. [18]
    Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, New York (1988)CrossRefGoogle Scholar
  19. [19]
    Ortony, A., Clore, L., Foss, A.: The referential structure of the affective lexicon. Cognitive Science 11, 341–364 (1987)CrossRefGoogle Scholar
  20. [20]
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends. Information Retrieval 1(1-2), 1–135 (2008)CrossRefGoogle Scholar
  21. [21]
    Patwardhan, S., Banerjee, S., Pedersen, T.: SenseRelate: TargetWord A generalized framework for word sense disambiguation. In: Proc. of AAAI 2005 (2005)Google Scholar
  22. [22]
    Picard, R.: Affective Computing. The MIT Press, Massachusetts (1997)CrossRefGoogle Scholar
  23. [23]
    Pinker, S.: The Language Instinct Perennial. HarperCollins (1994)Google Scholar
  24. [24]
    Shaikh, M.A.M., Prendinger, H., Mitsuru, I.: Rules of Emotions: A Linguistic Interpretation of an Emotion Model for Affect Sensing from Texts. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 737–738. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  25. [25]
    Shaikh, M., Ishizuka, M., Prendinger, H.: SenseNet: A Linguistic Tool to Visualize Numerical Valence Based Sentiment of Textual Data. In: Proc. ICON 2007 5th Int’l Conf. on Natural Language (2007)Google Scholar
  26. [26]
    Shaikh, M., Prendinger, H., Ishizuka, M.: Emotion Sensitive News Agent: An Approach Towards User Centric Emotion Sensing from the News. In: ACM International Conference on Web Intelligence, pp. 614–620 (2007)Google Scholar
  27. [27]
    Stone, J., Dunphy, D., Smith, M., Ogilvie, D.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)Google Scholar
  28. [28]
    Strapparava, C., Mihalcea, R.: SemEval-2007 Task 14: Affective Text. In: Proceedings of the 45th Aunual Meeting of ACL (2007)Google Scholar
  29. [29]
    Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of the 4th Internation Conference on Language Resources and Evaluation, Lisbon, pp. 1083–1086 (2004)Google Scholar
  30. [30]
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the HLT 2005 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Loureiro
    • 1
  • Goreti Marreiros
    • 1
  • José Neves
    • 2
  1. 1.ISEP, GECAD - Knowledge Engineering and Decision Support GroupPortugal
  2. 2.Minho UniversityPortugal

Personalised recommendations