Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes

  • Gregory Grefenstette
  • Yan Qu
  • David A. Evans
  • James G. Shanahan
Part of the The Information Retrieval Series book series (INRE, volume 20)

Abstract

In addition to factual content, many texts contain an emotional dimension. This emotive, or affect, dimension has not received a great amount of attention in computational linguistics until recently. However, now that messages (including spam) have become more prevalent than edited texts (such as newswire), recognizing this emotive dimension of written text is becoming more important. One resource needed for identifying affect in text is a lexicon of words with emotion-conveying potential. Starting from an existing affect lexicon and lexical patterns that invoke affect, we gathered a large quantity of text to measure the coverage of our existing lexicon. This chapter reports on our methods for identifying new candidate affect words and on our evaluation of our current affect lexicons. We describe how our affect lexicon can be extended based on results from these experiments.

Keywords

affect lexicon emotion lexicon discovery semantic axes 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

7. Bibliography

  1. Brave, S. and Nass, C. (2002) Emotion in human-computer interaction. In Jacko, J. and Sears, A. (Eds.) The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications. Lawrence Erlbaum Associates, Inc., Mahwah, NJ.Google Scholar
  2. Berlin, B. and Kay, P. (1969) Basic color terms: their universality and evolution. University of California Press, Berkeley.Google Scholar
  3. Church, K. W. and Hanks, P. (1989) Word association norms, mutual information and lexicography. In Proceedings of the 27th Annual Conference of the Association of Computational Linguistics. 76–82.Google Scholar
  4. Deese, J. (1964) The Associative Structure of some Common English Adjectives. Journal of Verbal Learning and Verbal Behavior 3(5). 347–357.CrossRefGoogle Scholar
  5. Hatzivassiloglou, V. and McKeown, K. R. (1993) Towards the automatic identification of adjectival scales: Clustering adjectives according to meaning. In Proceedings of 31st Annual Meeting of the Association for Computational Linguistics. 172–182.Google Scholar
  6. Hatzivassiloglou, V. and McKeown, K. R. (1997) Predicting the semantic orientation of adjectives. In Proceedings 35th Annual Meeting of the Association for Computational Linguistics. 174–181.Google Scholar
  7. Hindle, D. (1990) Noun classification from predicate argument structures. In Proceedings of the 28th Annual Meeting of the Association for Computational Linguistics. 268–275.Google Scholar
  8. Horn, L. (1969) A Presuppositional Analysis of Only and Even. In Papers from the 5th Regional Meeting of the Chicago Linguistics Society. 98–107.Google Scholar
  9. Huettner, A. and Subasic, P. (2000) Fuzzy Typing for Document Management. In ACL 2000 Software Demonstration.Google Scholar
  10. Lasswell, H. D. and Namenwirth, J. Z. (1969) The Lasswell Value Dictionary. Yale University Press, New Haven.Google Scholar
  11. Lehrer, A. (1974) Semantic Fields and Lexical Structure. North Holland, London.Google Scholar
  12. Levinson, S. C. (1983) Pragmatics. Cambridge University Press, Cambridge, UK.Google Scholar
  13. Stone, P. J., Dunphy, D. C., Smith, M. S., and Ogilvie, D. M. (1966) The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge, MA.Google Scholar
  14. Subasic, P. and Huettner, A. (2000a) Affect Analysis of Text Using Fuzzy Semantic Typing. In Proceedings of FUZZ-IEEE 2000.Google Scholar
  15. Subasic, P. and Huettner, A. (2000b) Calculus of Fuzzy Semantic Typing for Qualitative Analysis of Text. In Proceedings of ACM KDD 2000 Workshop on Text Mining.Google Scholar
  16. Subasic, P. and Huettner, A. (2001) Affect Analysis of Text Using Fuzzy Semantic Typing. IEEE Transactions on Fuzzy Systems, Special Issue.Google Scholar
  17. Teufel, S. and Moens, M. (2002) Summarizing Scientific Articles — Experiments with Relevance and Rhetorical Status. Computational Linguistics, 28(4).Google Scholar
  18. Turney, P. D. (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In Proceedings of the Twelfth European Conference on Machine Learning (ECML2001). 491–502.Google Scholar
  19. Turney, P. D. and Littman, M. L. (2003) Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4), 315–346.Google Scholar
  20. Wiebe, J. (2000) Learning subjective adjectives from corpora. In Proceedings of AAAI 2000. 735–740.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Gregory Grefenstette
    • 1
  • Yan Qu
    • 2
  • David A. Evans
    • 2
  • James G. Shanahan
    • 3
  1. 1.Commissariat à l’Energie Atomique, Centre de Fontenay-aux-RosesCEA/LIST/DTSI/SCRI/LIC2MFontenay-aux-Roses CedexFrance
  2. 2.Clairvoyance CorporationPittsburghUSA
  3. 3.Turn IncSan MateoUSA

Personalised recommendations