Polarity Preference of Verbs: What Could Verbs Reveal about the Polarity of Their Objects?

  • Manfred Klenner
  • Stefanos Petrakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7337)


The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb’s own prior polarity - for the same verb, these two properties might even be inverse. Polarity preferences of verbs are extracted on the basis of a large and dependency-parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas the upper bound goes up to 92%.


sentiment analysis polarity inference data mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354 (2005)Google Scholar
  2. 2.
    Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the Fifth Conference on Language Resources and Evaluation, pp. 417–422 (2006)Google Scholar
  3. 3.
    Clematide, S., Klenner, M.: Evaluation and extension of a polarity lexicon for German. In: Proceedings of the First Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 7–13 (2010)Google Scholar
  4. 4.
    Baroni, M., Bernardini, S., Ferraresi, A., Zanchetta, E.: The WaCky Wide Web: A collection of very large linguistically processed Web-crawled corpora. Language Resources and Evaluation, 209–226 (2009)Google Scholar
  5. 5.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2), 165–210 (2005)CrossRefGoogle Scholar
  6. 6.
    Sennrich, R., Schneider, G., Volk, M., Warin, M.: A new hybrid dependency parser for German. In: Proceedings of the German Society for Computational Linguistics and Language Technology, pp. 115–124 (2009)Google Scholar
  7. 7.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, pp. 174–181 (1997)Google Scholar
  8. 8.
    Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. (4), pp. 25–32 (2003)Google Scholar
  9. 9.
    Chesley, P., Vincent, B., Xu, L., Srihari, R.K.: Using Verbs and Adjectives to Automatically Classify Blog Sentiment. Training 580(263), 233 (2006)Google Scholar
  10. 10.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Semantically distinct verb classes involved in sentiment analysis. In: Proceedings of the International Conference on Applied Computing, pp. 27–34 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Manfred Klenner
    • 1
  • Stefanos Petrakis
    • 1
  1. 1.Institute for Computational LinguisticsUniversity of ZurichSwitzerland

Personalised recommendations