Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining

  • Diego Marcheggiani
  • Oscar Täckström
  • Andrea Esuli
  • Fabrizio Sebastiani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

A common feature of many online review sites is the use of an overall rating that summarizes the opinions expressed in a review. Unfortunately, these document-level ratings do not provide any information about the opinions contained in the review that concern a specific aspect (e.g., cleanliness) of the product being reviewed (e.g., a hotel). In this paper we study the finer-grained problem of aspect-oriented opinion mining at the sentence level, which consists of predicting, for all sentences in the review, whether the sentence expresses a positive, neutral, or negative opinion (or no opinion at all) about a specific aspect of the product. For this task we propose a set of increasingly powerful models based on conditional random fields (CRFs), including a hierarchical multi-label CRFs scheme that jointly models the overall opinion expressed in the review and the set of aspect-specific opinions expressed in each of its sentences. We evaluate the proposed models against a dataset of hotel reviews (which we here make publicly available) in which the set of aspects and the opinions expressed concerning them are manually annotated at the sentence level. We find that both hierarchical and multi-label factors lead to improved predictions of aspect-oriented opinions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baccianella, S., Esuli, A., Sebastiani, F.: Evaluation measures for ordinal regression. In: Proceedings of the 9th IEEE International Conference on Intelligent Systems Design and Applications (ISDA 2009), Pisa, IT, pp. 283–287 (2009)Google Scholar
  2. 2.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th Conference on Language Resources and Evaluation (LREC 2010), Valletta, MT (2010)Google Scholar
  3. 3.
    Bishop, C.M.: Pattern recognition and machine learning. Springer, Heidelberg (2006)MATHGoogle Scholar
  4. 4.
    Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM 2005), Bremen, DE, pp. 195–200 (2005)Google Scholar
  5. 5.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), Seattle, US, pp. 168–177 (2004)Google Scholar
  6. 6.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML 2001), Williamstown, US, pp. 282–289 (2001)Google Scholar
  7. 7.
    Lazaridou, A., Titov, I., Sporleder, C.: A Bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, BL, pp. 1630–1639 (2013)Google Scholar
  8. 8.
    Li, F., Han, C., Huang, M., Zhu, X., Xia, Y.J., Zhang, S., Yu, H.: Structure-aware review mining and summarization. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Bejing, CN, pp. 653–661 (2010)Google Scholar
  9. 9.
    Liu, B.: Sentiment analysis and opinion mining. Morgan & Claypool Publishers, San Rafael (2012)Google Scholar
  10. 10.
    McCallum, A., Schultz, K., Singh, S.: Factorie: Probabilistic programming via imperatively defined factor graphs. In: Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Vancouver, CA, pp. 1249–1257 (2009)Google Scholar
  11. 11.
    McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), Prague, CZ, pp. 432–439 (2007)Google Scholar
  12. 12.
    Moghaddam, S., Ester, M.: ILDA: Interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th ACM SIGIR International Conference on Research and Development in Information Retrieval (SIGIR 2011), Bejing, CN, pp. 665–674 (2011)Google Scholar
  13. 13.
    Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona, ES, pp. 271–278 (2004)Google Scholar
  14. 14.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 7th Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, US, pp. 79–86 (2002)Google Scholar
  15. 15.
    Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1966)Google Scholar
  16. 16.
    Täckström, O., McDonald, R.: Discovering fine-grained sentiment with latent variable structured prediction models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 368–374. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Titov, I., McDonald, R.T.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL 2008), Columbus, US, pp. 308–316 (2008)Google Scholar
  18. 18.
    Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: A rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, US, pp. 783–792 (2010)Google Scholar
  19. 19.
    Wick, M., Rohanimanesh, K., Bellare, K., Culotta, A., McCallum, A.: SampleRank: Training factor graphs with atomic gradients. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), Bellevue, US, pp. 777–784 (2011)Google Scholar
  20. 20.
    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 (HLT/EMNLP 2005), Vancouver, CA, pp. 347–354 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Diego Marcheggiani
    • 1
  • Oscar Täckström
    • 2
  • Andrea Esuli
    • 1
  • Fabrizio Sebastiani
    • 1
  1. 1.Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle RicerchePisaItaly
  2. 2.Swedish Institute of Computer ScienceKistaSweden

Personalised recommendations