Multi-view Ensemble Learning for Poem Data Classification Using SentiWordNet

  • Vipin KumarEmail author
  • Sonajharia Minz
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Poem is a piece of writing in which the expression of feeling and ideas is given intensity by particular attention to diction, rhythm and imagery [1]. In this modern age, the poem collection is ever increasing on the internet. Therefore, to classify poem correctly is an important task. Sentiment information of the poem is useful to enhance the classification task. SentiWordNet is an opinion lexicon. To each term are assigned two numeric scores indicating positive and negative sentiment information. Multiple views of the poem data may be utilized for learning to enhance the classification task. In this research, the effect of sentiment information has been explored for poem data classification using Multi-view ensemble learning. The experiments include the use of Support Vector Machine (SVM) for learning classifier corresponding to each view of the data.


Classification Multi-view Ensemble Learning Poem SentiWordNet 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Andrea, E., Sebastiani, F.: SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation, pp. 417–422 (2006)Google Scholar
  3. 3.
    Hamouda, A., Rohaim, M.: Reviews Classification Using SentiWordNet Lexicon. In: World Congress on Computer Science and Information Technology (2011)Google Scholar
  4. 4.
    Devitt, A., Ahmad, K.: Sentiment Polarity Identification in Financial News: A Cohesion- based Approach. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, pp. 984–991 (2007)Google Scholar
  5. 5.
    Aurangzeb, K., Baharum, B., Khairullah, K.: Sentence Based Sentiment Classification from Online Customer Reviews. In: FIT (2010)Google Scholar
  6. 6.
    Amini, M., Usunier, N., Goutte, C.: Learning from Multiple Partially Observed Views - An Application to Multilingual Text Categorization. In: Advances in Neural Information Processing Systems (2009)Google Scholar
  7. 7.
    Yuhong, G., Min, X.: Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning. In: Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK (2012)Google Scholar
  8. 8.
    Ping, G., QingSheng, Z., Cheng, Z.: A Multi-view Approach to Semi-supervised Document Classification with Incremental Naive Bayes. Computers & Mathematics with Applications 57(6), 1030–1036 (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Spyropoulos, C.D.: An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages. In: Proceedings of the 23rd Annual Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167 (2000)Google Scholar
  10. 10.
    Richter, G., MacFarlane, A.: The Impact of Metadata on the Accuracy of Automated Patent Classification. World Patent Information 37(3), 13–26 (2005)CrossRefGoogle Scholar
  11. 11.
    Moens, M.F., Dumortier, J.: Text Categorization: the Assignment of Subject Descriptors to Magazine Articles. Information Processing and Management 36(6), 841–861 (2000)CrossRefGoogle Scholar
  12. 12.
    Shih, L.K., Karger, D.R.: Learning Classifiers: Using URLs and Table Layout for Web Classification Tasks. In: Proceedings of the 13th International Conference on World Wide Web, pp. 193–202 (2004)Google Scholar
  13. 13.
    Yang, Y.: An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval 1(1-2), 67–88 (1999)Google Scholar
  14. 14.
    Scheffer, T.: Email Answering Assistance by Semi- Supervised Text Classification. Intelligent Data Analysis 8(5), 481–493 (2004)Google Scholar
  15. 15.
    Zhang, T., Oles, F.: Text Categorization Based on Regularized Linear Classifiers. Information Retrieval 4, 5–31 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Lee, P.Y., Hui, S.C., Fong, A.C.: Neural networks for web content filtering. IEEE Intelligent System 17(5), 48–57 (2002)CrossRefGoogle Scholar
  17. 17.
    Noraini, J., Masnizah, M., Shahrul, A.N.: Poetry Classification Using Support Vector Machines. Journal of Computer Science 8(9), 1441–1446 (2012)CrossRefGoogle Scholar
  18. 18.
    Tizhoosh, H.R., Dara, R.A.: On Poem Recognition. Pattern Analysis Application 9(4), 325–338 (2006)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Vipin, K., Sonajharia, M.: Poem Classification using Machine Learning Approach. In: Babu, B.V., Nagar, A., Deep, K., Bansal, M.P.J.C., Ray, K., Gupta, U. (eds.) SocPro 2012. AISC, vol. 236, pp. 675–682. Springer, Heidelberg (2014)Google Scholar
  20. 20.
    Yuhong, G., Min, X.: Cross Language Text Classification via Subspace Co-regularized Multi-view Learning. In: ICML (2012)Google Scholar
  21. 21.
    Massih-Reza, A., Nicolas, U., Cyril, G.: Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization. In: NIPS, pp. 28–36 (2009)Google Scholar
  22. 22.
    Vipin, K., Sonajharia, M.: Mood Classification of Lyrics using SentiWordNet. In: ICCCI 2012. IEEE Xplore (2013)Google Scholar
  23. 23.
    Kerstin, D.: Are SentiWordNet Scores Suited for Multi-Domain Sentiment Classification? In: ICDIM, pp. 33–38 (2009)Google Scholar
  24. 24.
    Chang, X., Dacheng, T., Chao, X.: A Survey on Multi-view Learning. CoRR abs/1304.5634 (2013)Google Scholar
  25. 25.
    Shiliang, S.: A Survey of Multi-view Machine Learning. Neural Computing & Application. Springer, London (2013)Google Scholar
  26. 26.
    Hotelling, H.: Relations between Two Sets of Variates. Biometrika 28, 321–377 (1936)CrossRefzbMATHGoogle Scholar
  27. 27.
    Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  28. 28.
    Wei, B., Pal, C.: Cross Lingual Adaptation: An Experiment on Sentiment Classification. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 258–262. Association for Computational Linguistics (2010)Google Scholar
  29. 29.
    Wan, C., Pan, R., Li, J.: Bi-weighting Domain Adaptation for Cross-language Text Classification. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)Google Scholar
  30. 30.
    Massih-Reza, A., Nicolas, U., Cyril, G.: Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization. In: NIPS 2009, pp. 28–36 (2009)Google Scholar
  31. 31.
    Wan, X.: Co-training for Cross-lingual Sentiment Classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 1, pp. 235–243. Association for Computational Linguistics (2009)Google Scholar
  32. 32.
    Amini, M.R., Goutte, C.: A Co-classification Approach to Learning from Multilingual Corpora. Machine Learning 79(1), 105–121 (2010)CrossRefMathSciNetGoogle Scholar
  33. 33.
    Chen, Q., Sun, S.: Hierarchical Multi-view Fisher Discriminant Analysis. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 289–298. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  34. 34.
    Sun, S.: Multi-view Laplacian Support Vector Machines. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part II. LNCS, vol. 7121, pp. 209–222. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  35. 35.
    Xu, Z., Sun, S.: An Algorithm on Multi-view Adaboost. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 355–362. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  36. 36.
    Muslea, I., Minton, S., Knoblock, C.: Active Learning with Multiple Views. Journal of Artificial Intelligence Ressearch 27, 203–233 (2006)zbMATHMathSciNetGoogle Scholar
  37. 37.
    Xu, Z., Sun, S.: Multi-view Transfer Learning with Adaboost. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence, pp. 399–340 (2011)Google Scholar
  38. 38.
    De Sa, V., Gallagher, P., Lewis, J., Malave, V.: Multi-view Kernel Construction. Machine Learning 79, 47–71 (2010)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Chen, X., Liu, H., Carbonell, J.: Structured Sparse Canonical Correlation Analysis. In: Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, pp. 199–207 (2012)Google Scholar
  40. 40.
  41. 41.
    Jiawei, H., Micheline, K.: Data Mining Concepts and Techniques, 2nd edn. Elsevier (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations