MPQA Opinion Corpus

Chapter

Abstract

The MPQA Opinion Corpus is a collection of documents with expression-level, multi-attribute annotations of opinions, sentiments, and other private states. This chapter describes the MPQA annotation scheme and the development of the MPQA Corpus.

Keywords

Sentiment Subjectivity opinion Case study Expression-level annotation 

References

  1. 1.
    Abdul-Mageed, M., Diab, M.T., Korayem, M.: Subjectivity and sentiment analysis of modern standard Arabic. In: Proceeding of the 49th Annual Meeting of the Association for Computational Linguistics. Human Language Technologies, (Vol. 2: Short Papers), (2011)Google Scholar
  2. 2.
    Akkaya, C., Wiebe, J., Conrad, A., Mihalcea, R.: Improving the impact of subjectivity word sense disambiguation on contextual opinion analysis. In: Proceeding of the 15th Conference on Computational Natural Language Learning, (2011)Google Scholar
  3. 3.
    Andrea Esuli, F.S., Urciuoli, I.: Annotating expressions of opinion and emotion in the Italian Content Annotation Bank. In: Proceeding of the 6th International Language Resources and Evaluation, (2008)Google Scholar
  4. 4.
    Asher, N.: Belief in discourse representation theory. J. Philos. Logic 15, 127–189 (1986)CrossRefGoogle Scholar
  5. 5.
    Banfield, A.: Unspeakable Sentences. Routledge and Kegan Paul, Boston (1982)Google Scholar
  6. 6.
    Chatman, S.: Story and Discourse: Narrative Structure in Fiction and Film. Cornell University Press, New York (1978)Google Scholar
  7. 7.
    Clematide, S., Gindl, S., Klenner, M., Petrakis, S., Remus, R., Ruppenhofer, J., Waltinger, U., Wiegand, M.: Mlsa a multi-layered reference corpus for german sentiment analysis. In: Proceeding of the 8th International Conference on Language Resources and Evaluation, (2012)Google Scholar
  8. 8.
    Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960)CrossRefGoogle Scholar
  9. 9.
    Cohn, D.: Transparent Minds: Narrative Modes for Representing Consciousness in Fiction. Princeton University Press, New Jersey (1978)Google Scholar
  10. 10.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: a framework and graphical development environment for robust NLP tools and applications. In: Proceeding of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania (2002)Google Scholar
  11. 11.
    Doležel, L.: Narrative Modes in Czech Literature. University of Toronto Press, Canada (1973)Google Scholar
  12. 12.
    Fauconnier, G.: Mental Spaces: Aspects of Meaning Construction in Natural Language. MIT Press, Cambridge (1985)Google Scholar
  13. 13.
    Fodor, J.D.: The Linguistic Description of Opaque Contexts. Outstanding dissertations in linguistics 13. Garland, New York (1979)Google Scholar
  14. 14.
    Ghosh, S., Tonelli, S., Johansson, R.: Mining fine-grained opinion expressions with shallow parsing. In: Proceeding of the International Conference Recent Advances in Natural Language Processing, (2013)Google Scholar
  15. 15.
    Halliday, M.: (1985/1994) An Introduction to Functional Grammar. London, Edward ArnoldGoogle Scholar
  16. 16.
    Hermann, K.M., Blunsom, P.: The role of syntax in vector space models of compositional semantics. In: Proceeding of the 51st Annual Meeting of the Association for Computational Linguistics, (Vol. 1: Long Papers), (2013)Google Scholar
  17. 17.
    Johansson, R., Moschitti, A.: Relational features in fine-grained opinion analysis. Comput. Linguist. 39(3), 473–509 (2013)CrossRefGoogle Scholar
  18. 18.
    Kessler, J.S., Eckert, M., Clark, L., Nicolov, N.: The 2010 ICWSM JDPA Sentment Corpus for the automotive domain. In: 4th International AAAI Conference on Weblogs and Social Media Data Workshop Challenge (ICWSM-DWC 2010), (2010)Google Scholar
  19. 19.
    Kim, J., Li, J.J., Lee, J.H.: Evaluating multilanguage-comparability of subjectivity analysis systems. In: Proceeding of the 48th Annual Meeting of the Association for Computational Linguistics, (2010)Google Scholar
  20. 20.
    Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceeding of the 20th International Conference on Computational Linguistics (COLING 2004), (2004)Google Scholar
  21. 21.
    Krippendorff, K.: Content Analysis: An Introduction to its Methodology. Sage Publications, Beverly Hills (1980)Google Scholar
  22. 22.
    Kuroda, S.Y.: Where epistemology, style and grammar meet: a case study from the Japanese. In: Kiparsky P., Anderson S. (eds.) A Festschrift for Morris Halle, pp. 377–391. Holt, Rinehart Winston, New York (1973)Google Scholar
  23. 23.
    Kuroda, S.Y.: Reflections on the foundations of narrative theory-from a linguistic point of view. In: van Dijk, T. (ed.) Pragmatics of Language and Literature, pp. 107–140. North-Holland, Amsterdam (1976)Google Scholar
  24. 24.
    Lan, M., Xu, Y., Niu, Z.: Leveraging synthetic discourse data via multi-task learning for implicit discourse relation recognition. In: Proceeding of the 51st Annual Meeting of the Association for Computational Linguistics, (vol. 1: Long Papers) (2013)Google Scholar
  25. 25.
    Lin, C., He, Y., Everson, R.: Sentence subjectivity detection with weakly-supervised learning. In: Proceeding of 5th International Joint Conference on Natural Language Processing, (2011)Google Scholar
  26. 26.
    Martin, J.: English Text: System and Structure. John Benjamins, Amsterdam (1992)CrossRefGoogle Scholar
  27. 27.
    Martin, J.: Beyond exchange: APPRAISAL systems in English. In: Hunston, S., Thompson, G. (eds.) Evaluation in Text: Authorial stance and the construction of discourse, pp. 142–175. Oxford University Press, Oxford (2000)Google Scholar
  28. 28.
    Meng, X., Wei, F., Liu, X., Zhou, M., Xu, G., Wang, H.: Cross-lingual mixture model for sentiment classification. In: Proceeding of the 50th Annual Meeting of the Association for Computational Linguistics, (vol. 1: Long Papers) (2012)Google Scholar
  29. 29.
    Mohtarami, M., Lan, M., Tan, C.L.: Probabilistic sense sentiment similarity through hidden emotions. In: Proceeding of the 51st Annual Meeting of the Association for Computational Linguistics, (vol. 1: Long Papers), (2013)Google Scholar
  30. 30.
    Picard, R.: Affective Computing. MIT Press, Cambridge (1997)Google Scholar
  31. 31.
    Quirk, R., Greenbaum, S., Leech, G., Svartvik, J.: A Comprehensive Grammar of the English Language. Longman, New York (1985)Google Scholar
  32. 32.
    Rapaport, W.: Logical foundations for belief representation. Cogn. Sci. 10, 371–422 (1986)CrossRefGoogle Scholar
  33. 33.
    Ruppenhofer, J., Rehbein, I.: Yes we can!? Annotating English modal verbs. In: Proceeding of the 8th International Conference on Language Resources and Evaluation, (2012)Google Scholar
  34. 34.
    Seki, Y., Evans, D.K., Ku, L.W., Sun, L., Chen, H.H., Kando, N.: Overview of multilingual opinion analysis task at NTCIR-7. In: Proceeding of NTCIR-7, (2008)Google Scholar
  35. 35.
    Shin, H., Kim, M., Jang, H., Cattle, A.: Annotation scheme for constructing sentiment corpus in Korean. In: Proceeding of the 26th Pacific Asia Conference on Language, Information, and Computation, (2012)Google Scholar
  36. 36.
    Stoyanov, V., Cardie, C., Wiebe, J.: Multi-perspective question answering using the OpQA corpus. In: Proceeding of the Human Language Technologies Conference/Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-2005), (2005)Google Scholar
  37. 37.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 1–308 (2011)CrossRefGoogle Scholar
  38. 38.
    Toprak, C., Jakob, N., Gurevych, I.: Sentence and expression level annotation of opinions in user-generated discourse. In: Proceeding of the 48th Annual Meeting of the Association for Computational Linguistics, (2010)Google Scholar
  39. 39.
    Uspensky, B.: A Poetics of Composition. University of California Press, California (1973)Google Scholar
  40. 40.
    Wang, S., Manning, C.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceeding of the 50th Annual Meeting of the Association for Computational Linguistics, (vol. 2: Short Papers), (2012)Google Scholar
  41. 41.
    White, P.: Appraisal: the language of attitudinal evaluation and intersubjective stance. In: Verschueren J., Ostman J., blommaert J., Bulcaen C. (eds.) The Handbook of Pragmatics, Amsterdam/Philadelphia: John Benjamins Publishing Company, pp 1–27. (2002)Google Scholar
  42. 42.
    Wiebe, J.: Recognizing subjective sentences: a computational investigation of narrative text. Ph.D. Thesis, State University of New York at Buffalo (1990)Google Scholar
  43. 43.
    Wiebe, J.: Tracking point of view in narrative. Comput. Linguist. 20(2), 233–287 (1994)Google Scholar
  44. 44.
    Wiebe, J.: Instructions for annotating opinions in newspaper articles. Department of Computer Science Technical Report TR-02-101, University of Pittsburgh (2002)Google Scholar
  45. 45.
    Wiebe, J., Breck, E., Buckley, C., Cardie, C., Davis, P., Fraser, B., Litman, D., Pierce, D., Riloff, E., Wilson, T., Day, D., Maybury, M.: Recognizing and organizing opinions expressed in the world press. In: Working Notes of the AAAI Spring Symposium in New Directions in Question Answering, Palo Alto, California (2003)Google Scholar
  46. 46.
    Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30(3), 277–308 (2004)CrossRefGoogle Scholar
  47. 47.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Res. Eval. (formerly Computers and the Humanities) 39(2/3), 164–210 (2005)Google Scholar
  48. 48.
    Wiegand, M., Klakow, D.: Generalization methods for in-domain and cross-domain opinion holder extraction. In: Proceeding of the 13th Conference of the European Chapter of the Association for Computational Linguistics, (2012)Google Scholar
  49. 49.
    Wilks, Y., Bien, J.: Beliefs, points of view and multiple environments. Cogn. Sci. 7, 95–119 (1983)CrossRefGoogle Scholar
  50. 50.
    Wilson, T.: Annotating subjective content in meetings. In: Proceeding of the 6th Language Resources and Evaluations Conference, (2008)Google Scholar
  51. 51.
    Wilson, T.: Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. Ph.D. Thesis, Intelligent Systems Program, University of Pittsburgh (2008)Google Scholar
  52. 52.
    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
  53. 53.
    Xiao, M., Guo, Y.: Multi-view AdaBoost for multilingual subjectivity analysis. In: Proceeding of the 24th International Conference on Computational Linguistics, (2012)Google Scholar
  54. 54.
    Yang, B., Cardie, C.: Joint inference for fine-grained opinion extraction. In: Proceeding of the 51st Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), (2013)Google Scholar
  55. 55.
    Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceeding of the Conference on Empirical Methods in Natural Language Processing, (2003)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Hanover CollegeHanoverUSA
  2. 2.University of PittsburghPittsburghUSA
  3. 3.Cornell UniversityIthacaUSA

Personalised recommendations