An Ensemble Model for Cross-Domain Polarity Classification on Twitter

  • Adam Tsakalidis
  • Symeon Papadopoulos
  • Ioannis Kompatsiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8787)

Abstract

Polarity analysis of Social Media content is of significant importance for various applications. Most current approaches treat this task as a classification problem, demanding a labeled corpus for training purposes. However, if the learned model is applied on a different domain, the performance drops significantly and, given that it is impractical to have labeled corpora for every domain, this becomes a challenging task. In the current work, we address this problem, by proposing an ensemble classifier that is trained on a general domain and and adapts, without the need for additional ground truth, on the desired (test) domain before classifying a document. Our experiments are performed on three different datasets and the obtained results are compared with various baselines and state-of-the-art methods; we demonstrate that our model is outperforming all out-of-domain trained baseline algorithms, and that it is even comparable with different in-domain classifiers.

Keywords

Sentiment analysis polarity detection ensemble classifier 

References

  1. 1.
    Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (2011)Google Scholar
  2. 2.
    Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of twitter. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. ISWC 2012, pp. 508–524. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 36–44. ACL (2010)Google Scholar
  4. 4.
    Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1833–1836. ACM (2010)Google Scholar
  5. 5.
    Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 27–38. ACM (2013)Google Scholar
  6. 6.
    Schinas, E., Papadopoulos, S., Diplaris, S., Kompatsiaris, Y., Mass, Y., Herzig, J., Boudakidis, L.: Eventsense: Capturing the pulse of large-scale events by mining social media streams. In: Proceedings of the 17th Panhellenic Conference on Informatics, pp. 17–24. ACM (2013)Google Scholar
  7. 7.
    Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., Liu, B.: Combining lexicon-based and learning-based methods for twitter sentiment analysis. HP Laboratories, Technical Report HPL-2011 (2011)Google Scholar
  8. 8.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In: ICWSM, vol. 10, pp. 178–185 (2010)Google Scholar
  9. 9.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 1–15. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151–160. ACL (2011)Google Scholar
  11. 11.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1–12 (2009)Google Scholar
  12. 12.
    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. ACL (2005)Google Scholar
  13. 13.
    Speriosu, M., Sudan, N., Upadhyay, S., Baldridge, J.: Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the First Workshop on Unsupervised Learning in NLP, pp. 53–63. ACL (2011)Google Scholar
  14. 14.
    Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. ACL (2005)Google Scholar
  15. 15.
    Brody, S., Diakopoulos, N.: Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 562–570. ACL (2011)Google Scholar
  16. 16.
    Andreevskaia, A., Bergler, S.: When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging. In: Proceedings of ACL 2008, pp. 290–298. ACL (2008)Google Scholar
  17. 17.
    Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, pp. 417–422 (2006)Google Scholar
  18. 18.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  19. 19.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 173–180 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Adam Tsakalidis
    • 1
  • Symeon Papadopoulos
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies InstituteCERTHThessalonikiGreece

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