Advertisement

A Cross-Lingual Approach for Building Multilingual Sentiment Lexicons

  • Behzad Naderalvojoud
  • Behrang Qasemizadeh
  • Laura Kallmeyer
  • Ebru Akcapinar Sezer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11107)

Abstract

We propose a cross-lingual distributional model to build sentiment lexicons in many languages from resources available in English. We evaluate this method for two languages, German and Turkish, and on several datasets. We show that the sentiment lexicons built using our method remarkably improve the performance of a state-of-the-art lexicon-based BiLSTM sentiment classifier.

References

  1. 1.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC (2010)Google Scholar
  2. 2.
    Cieliebak, M., Deriu, J., Egger, D., Uzdilli, F.: A twitter corpus and benchmark resources for German sentiment analysis. In: SocialNLP 2017, p. 45 (2017)Google Scholar
  3. 3.
    Das, A., Bandyopadhyay, S.: SentiWordNet for Indian languages. In: ALR (2010)Google Scholar
  4. 4.
    Demirtas, E., Pechenizkiy, M.: Cross-lingual polarity detection with machine translation. In: WISDOM, p. 9. ACM (2013)Google Scholar
  5. 5.
    Esuli, A., Sebastiani, F.: Determining term subjectivity and term orientation for opinion mining. In: EACL, vol. 6, p. 2006 (2006)Google Scholar
  6. 6.
    Hung, C.: Word of mouth quality classification based on contextual sentiment lexicons. Inf. Process. Manag. 53(4), 751–763 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hung, C., Chen, S.J.: Word sense disambiguation based sentiment lexicons for sentiment classification. Knowl.-Based Syst. 110, 224–232 (2016)CrossRefGoogle Scholar
  8. 8.
    Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: COLING (2004)Google Scholar
  9. 9.
    Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)Google Scholar
  10. 10.
    Mohammad, S., Dunne, C., Dorr, B.: Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In: EMNLP. ACL (2009)Google Scholar
  11. 11.
    Strapparava, C., Valitutti, A., Stock, O.: The affective weight of lexicon. In: LREC (2006)Google Scholar
  12. 12.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based splncs04 methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  13. 13.
    Takamura, H., Inui, T., Okumura, M.: Extracting semantic orientations of words using spin model. In: ACL, pp. 133–140. ACL (2005)Google Scholar
  14. 14.
    Teng, Z., Vo, D.T., Zhang, Y.: Context-sensitive lexicon features for neural sentiment analysis. In: EMNLP, pp. 1629–1638 (2016)Google Scholar
  15. 15.
    Tiedemann, J.: News from opus-a collection of multilingual parallel corpora with tools and interfaces. In: RNLP, vol. 5, pp. 237–248 (2009)Google Scholar
  16. 16.
    Turney, P.D.: Similarity of semantic relations. Comput. Linguist. 32(3), 379–416 (2006)CrossRefGoogle Scholar
  17. 17.
    Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. TOIS 21(4), 315–346 (2003)CrossRefGoogle Scholar
  18. 18.
    Ucan, A., Naderalvojoud, B., Sezer, E.A., Sever, H.: SentiWordNet for new language: automatic translation approach. In: SITIS, pp. 308–315. IEEE (2016)Google Scholar
  19. 19.
    Waltinger, U.: GermanPolarityClues: a lexical resource for German sentiment analysis. In: LREC. Electronic Proceedings, Valletta, Malta, May 2010Google Scholar
  20. 20.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: EMNLP. pp. 347–354. ACL (2005)Google Scholar
  21. 21.
    Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In: AAAI, vol. 4, pp. 761–769 (2004)Google Scholar
  22. 22.
    Wojatzki, M., Ruppert, E., Holschneider, S., Zesch, T., Biemann, C.: Germeval 2017: shared task on aspect-based sentiment in social media customer feedback. In: GermEval (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Hacettepe UniversityBeytepe, AnkaraTurkey
  2. 2.DFG SFB 991Universität DüsseldorfDüsseldorfGermany

Personalised recommendations