From Conditional Random Field (CRF) to Rhetorical Structure Theory(RST): Incorporating Context Information in Sentiment Analysis

  • Aggeliki Vlachostergiou
  • George Marandianos
  • Stefanos Kollias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

Abstract

This paper investigates a method based on Conditional Random Fields (CRFs) to incorporate sentence structure (syntax and semantics) and context information to identify sentiments of sentences. It also demonstrates the usefulness of the Rhetorical Structure Theory (RST) taking into consideration the discourse role of text segments. Thus, this paper’s aim is to reconsider the effectiveness of CRF and RST methods in incorporating the contextual information into Sentiment Analysis systems. Both methods are evaluated on two, different in size and genre of information sources, the Movie Review Dataset and the Finegrained Sentiment Dataset (FSD). Finally, we discuss the lessons learned from these experimental settings w.r.t. addressing the following key research questions such as whether there is an appropriate type of social media repository to incorporate contextual information, whether extending the pool of the selected features could improve context incorporation into SA systems and which is the best performing feature combination to achieve such improved performance.

Keywords

HCI Sentiment analysis Context RST Context-aware sentiment analysis systems Movie reviews dataset FSD collection 

References

  1. 1.
    Appel, O., Chiclana, F., Carter, J.: Main concepts, state of the art and future research questions in sentiment analysis. Acta Polytech. Hung. 12(3), 87–108 (2015)Google Scholar
  2. 2.
    Palmero Aprosio, A., Corcoglioniti, F., Dragoni, M., Rospocher, M.: Supervised opinion frames detection with RAID. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 251–263. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25518-7_22 CrossRefGoogle Scholar
  3. 3.
    Bal, D., Bal, M., Bunningen, A., Hogenboom, A., Hogenboom, F., Frasincar, F.: Sentiment analysis with a multilingual pipeline. In: Bouguettaya, A., Hauswirth, M., Liu, L. (eds.) WISE 2011. LNCS, vol. 6997, pp. 129–142. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24434-6_10 CrossRefGoogle Scholar
  4. 4.
    Beineke, P., Hastie, T., Manning, C., Vaithyanathan, S.: Exploring sentiment summarization. In: Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, vol. 39 (2004)Google Scholar
  5. 5.
    Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 355–362. Association for Computational Linguistics (2005)Google Scholar
  6. 6.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)CrossRefGoogle Scholar
  7. 7.
    Dragoni, M., Tettamanzi, A.G., Pereira, C.D.C.: Dranziera: an evaluation protocol for multi-domain opinion mining. In: 10th International Conference on Language Resources and Evaluation (LREC 2016), pp. 267–272. European Language Resources Association (ELRA) (2016)Google Scholar
  8. 8.
    Federici, M., Dragoni, M.: Towards unsupervised approaches for aspects extractionGoogle Scholar
  9. 9.
    Federici, M., Dragoni, M.: A knowledge-based approach for aspect-based opinion mining. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 141–152. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46565-4_11 CrossRefGoogle Scholar
  10. 10.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)CrossRefGoogle Scholar
  11. 11.
    Gangemi, A., Presutti, V., Recupero, D.R.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput. Intell. Mag. 9(1), 20–30 (2014)CrossRefGoogle Scholar
  12. 12.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, pp. 174–181. ACL (1997)Google Scholar
  13. 13.
    Heerschop, B., Goossen, F., Hogenboom, A., Frasincar, F., Kaymak, U., de Jong, F.: Polarity analysis of texts using discourse structure. In: 20th International Conference on Information and Knowledge Management, pp. 1061–1070. ACM (2011)Google Scholar
  14. 14.
    Hogenboom, A., Hogenboom, F., Frasincar, F., Schouten, K., van der Meer, O.: Semantics-based information extraction for detecting economic events. Multimedia Tools Appl. 64(1), 27–52 (2013)CrossRefGoogle Scholar
  15. 15.
    Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045. Association for Computational Linguistics (2010)Google Scholar
  16. 16.
    Ji, Y., Eisenstein, J.: Representation learning for text-level discourse parsing. In: ACL (1), pp. 13–24 (2014)Google Scholar
  17. 17.
    Katz, G., Ofek, N., Shapira, B.: Consent: context-based sentiment analysis. Knowl. Based Syst. 84, 162–178 (2015)CrossRefGoogle Scholar
  18. 18.
    Kim, S.M., Hovy, E.: Automatic identification of pro and con reasons in online reviews. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 483–490. Association for Computational Linguistics (2006)Google Scholar
  19. 19.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning ICML, vol. 1, pp. 282–289 (2001)Google Scholar
  20. 20.
    Mann, W.C., Thompson, S.A.: Rhetorical structure theory: toward a functional theory of text organization. Text-Interdisc. J. Study Discourse 8(3), 243–281 (1988)CrossRefGoogle Scholar
  21. 21.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)Google Scholar
  22. 22.
    McCallum, A.: Efficiently inducing features of conditional random fields. In: Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, pp. 403–410. Morgan Kaufmann Publishers Inc. (2002)Google Scholar
  23. 23.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)Google Scholar
  24. 24.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)Google Scholar
  25. 25.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  26. 26.
    Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: frame-based sentiment analysis. Cognitive Comput. 7(2), 211–225 (2015)CrossRefGoogle Scholar
  27. 27.
    Rexha, A., Kröll, M., Dragoni, M., Kern, R.: Exploiting propositions for opinion mining. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 121–125. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46565-4_9 CrossRefGoogle Scholar
  28. 28.
    Rexha, A., Kröll, M., Dragoni, M., Kern, R.: Polarity classification for target phrases in tweets: a word2Vec approach. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 217–223. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47602-5_40 CrossRefGoogle Scholar
  29. 29.
    Soricut, R., Marcu, D.: Sentence level discourse parsing using syntactic and lexical information. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 149–156. Association for Computational Linguistics (2003)Google Scholar
  30. 30.
    Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)Google Scholar
  31. 31.
    Taboada, M., Voll, K., Brooke, J.: Extracting sentiment as a function of discourse structure and topicality. Simon Fraser Univeristy School of Computing Science Technical Report (2008)Google Scholar
  32. 32.
    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).  https://doi.org/10.1007/978-3-642-20161-5_37 CrossRefGoogle Scholar
  33. 33.
    Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)Google Scholar
  34. 34.
    Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005).  https://doi.org/10.1007/978-3-540-30586-6_53 CrossRefGoogle Scholar
  35. 35.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2–3), 165–210 (2005)CrossRefGoogle Scholar
  36. 36.
    Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M.: Fine-grained sentiment analysis with structural features. In: IJCNLP, pp. 336–344 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aggeliki Vlachostergiou
    • 1
  • George Marandianos
    • 1
  • Stefanos Kollias
    • 2
  1. 1.National Technical University of AthensZografouGreece
  2. 2.University of LincolnLincolnUK

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