Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis

  • Łukasz AugustyniakEmail author
  • Krzysztof Rajda
  • Tomasz Kajdanowicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10191)


This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototype solution of data flow with interesting and valuable results. The proposed method’s results proved the high accuracy of aspect detection when applied to the gold standard dataset.


Sentiment analysis Opinion mining Aspect-based sentiment analysis Rhetorical analysis Rhetorical Structure Theory 



The work was partially supported by the National Science Centre grants DEC-2016/21/N/ST6/02366 and DEC-2016/21/D/ST6/02948, and from the European Unions Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 691152 (RENOIR project).


  1. 1.
    Augustyniak, Ł., Szymański, P., Kajdanowicz, T., Tuligłowicz, W.: Comprehensive study on Lexicon-based ensemble classification sentiment analysis. Entropy 18(1), 4 (2015)CrossRefGoogle Scholar
  2. 2.
    Danlos, L.: D-STAG: a formalism for discourse analysis based on SDRT and using synchronous TAG. In: Groote, P., Egg, M., Kallmeyer, L. (eds.) FG 2009. LNCS (LNAI), vol. 5591, pp. 64–84. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-20169-1_5 CrossRefGoogle Scholar
  3. 3.
    De Clercq, O., Van de Kauter, M., Lefever, E., Hoste, V.: LT3: applying hybrid terminology extraction to aspect-based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation - SemEval 2015, vol. 1997, pp. 719–724 (2015)Google Scholar
  4. 4.
    Feng, V.W., Hirst, G.: Two-pass discourse segmentation with pairing and global features. ArXiv e-prints 1407.8215, July 2014
  5. 5.
    Guzmán, F., Joty, S., Màrquez, L., Nakov, P.: Using discourse structure improves machine translation evaluation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 687–698 (2014)Google Scholar
  6. 6.
    Joty, S., Carenini, G., Ng, R.T.: A novel discriminative framework for sentence-level discourse analysis (2012)Google Scholar
  7. 7.
    Joty, S., Carenini, G., Ng, R.T.: CODRA: a novel discriminative framework for rhetorical analysis. Comput. Linguist. 41(3), 1–50 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, pp. 21:0–21:934 (2009)Google Scholar
  9. 9.
    Lazaridou, A., Titov, I., Sporleder, C.: A Bayesian model for joint unsupervised induction of sentiment, aspect, discourse representations. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1630–1639 (2013)Google Scholar
  10. 10.
    Liu, B.: Sentiment analysis and subjectivity (2010)Google Scholar
  11. 11.
    Liu, Q., Gao, Z., Liu, B., Zhang, Y.: Automated rule selection for aspect extraction in opinion mining. In: International Joint Conference on Artificial Intelligence, IJCAI 2015, pp. 1291–1297 (2015)Google Scholar
  12. 12.
    Louis, A., Joshi, A.K., Nenkova, A., Louis, C., Joshi, A.: Discourse indicators for content selection in summaization, pp. 147–156 (2010)Google Scholar
  13. 13.
    Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: toward a functional theory of text organization (1988)Google Scholar
  14. 14.
    Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 Task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, pp. 486–495 (2015)Google Scholar
  15. 15.
    Martin, J.R.: English Text. John Benjamins Publishing Company, Amsterdam (1992)CrossRefGoogle Scholar
  16. 16.
    McAuley, J., Leskovec, J.: Hidden factors, hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems - RecSys 2013, pp. 165–172 (2013)Google Scholar
  17. 17.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. World Wide Web Internet Web Inf. Syst. 54(1999–66), 1–17 (1998)Google Scholar
  18. 18.
    Taboada, M.: Discourse markers as signals (or not) of rhetorical relations. J. Pragmatics 38(4), 567–592 (2006)CrossRefGoogle Scholar
  19. 19.
    Wagner, J., Arora, P., Cortes, S., Barman, U., Bogdanova, D., Foster, J., Tounsi, L.: DCU: aspect-based polarity classification for SemEval task 4. In: Proceedings of the 8th International Workshop on Semantic Evaluation - SemEval 2014, pp. 223–229 (2014)Google Scholar
  20. 20.
    Wang, B., Liu, M.: Deep learning for aspect-based sentiment analysis, pp. 1–9 (2015)Google Scholar
  21. 21.
    Wen, T-H., Gasic, M., Mrksic, N., Su, P-H., Vandyke, D., Young, S.: Semantically conditioned LSTM-based natural language generation for spoken dialogue systems. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1711–1721, September 2015Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Łukasz Augustyniak
    • 1
    Email author
  • Krzysztof Rajda
    • 2
  • Tomasz Kajdanowicz
    • 1
  1. 1.Department of Computational IntelligenceWroclaw University of TechnologyWroclawPoland
  2. 2.Kenaz TechnologiesLesznoPoland

Personalised recommendations