Sentiment Attitudes and Their Extraction from Analytical Texts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11107)


In this paper we study the task of extracting sentiment attitudes from analytical texts. We experiment with the RuSentRel corpus containing annotated Russian analytical texts in the sphere of international relations. Each document in the corpus is annotated with sentiments from the author to mentioned named entities, and attitudes between mentioned entities. We consider the problem of extracting sentiment relations between entities for the whole documents as a three-class machine learning task.


Sentiment analysis Coherent texts 


  1. 1.
    Amigó, E., et al.: Overview of RepLab 2013: evaluating online reputation monitoring systems. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 333–352. Springer, Heidelberg (2013). Scholar
  2. 2.
    Ben-Ami, Z., Feldman, R., Rosenfeld, B.: Entities’ sentiment relevance. In: ACL 2014, vol. 2, pp. 87–92 (2014)Google Scholar
  3. 3.
    Ben-Ami, Z., Feldman, R., Rosenfeld, B.: Exploiting the focus of the document for enhanced entities’ sentiment relevance detection. In: 2015 IEEE International Conference on Workshop (ICDMW), pp. 1284–1293. IEEE (2015)Google Scholar
  4. 4.
    Choi, E., Rashkin, H., Zettlemoyer, L., Choi, Y.: Document-level sentiment inference with social, faction, and discourse context. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 333–343. ACL (2016)Google Scholar
  5. 5.
    Deng, L., Wiebe, J.: MPQA 3.0: an entity/event-level sentiment corpus. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1323–1328 (2015)Google Scholar
  6. 6.
    Ellis, J., Getman, J., Strassel, S.: Overview of linguistic resources for the TAC KBP 2014 evaluations: planning, execution, and results. In: Proceedings of TAC KBP 2014 Workshop, National Institute of Standards and Technology, pp. 17–18 (2014)Google Scholar
  7. 7.
    Kutuzov, A., Kuzmenko, E.: WebVectors: a toolkit for building web interfaces for vector semantic models. In: Ignatov, D.I., et al. (eds.) AIST 2016. CCIS, vol. 661, pp. 155–161. Springer, Cham (2017). Scholar
  8. 8.
    Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, Boston (2012). Scholar
  9. 9.
    Loukachevitch, N., Blinov, P., Kotelnikov, E., Rubtsova, Y., Ivanov, V., Tutubalina, E.: SentiRuEval: testing object-oriented sentiment analysis systems in Russian. In: Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog, vol. 2, pp. 2–13 (2015)Google Scholar
  10. 10.
    Loukachevitch, N., Rusnachenko, N.: Extracting sentiment attitudes from analytical texts. In: Proceedings of International Conference Dialog (2018)Google Scholar
  11. 11.
    Loukachevitch, N.V., Rubtsova, Y.V.: SentiRuEval-2016: overcoming time gap and data sparsity in tweet sentiment analysis. In: Computational Linguistics and Intellectual Technologies Proceedings of the Annual International Conference Dialogue, Moscow, RGGU, pp. 416–427 (2016)Google Scholar
  12. 12.
    Loukachevitch, N., Levchik, A.: Creating a general Russian sentiment lexicon. In: Proceedings of LREC (2016)Google Scholar
  13. 13.
    Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. (TOIT) 17, 26 (2017)CrossRefGoogle Scholar
  14. 14.
    Mozharova, V.A., Loukachevitch, N.V.: Combining knowledge and CRF-based approach to named entity recognition in Russian. In: Ignatov, D.I., et al. (eds.) AIST 2016. CCIS, vol. 661, pp. 185–195. Springer, Cham (2017). Scholar
  15. 15.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In proceedings of LREC, pp. 1320–1326 (2010)Google Scholar
  16. 16.
    Rosenthal, S., Farra, N., Nakov, P.: SemEval-2017 task 4: sentiment analysis in twitter. In: Proceedings of SemEval-2017 Workshop, pp. 502–518 (2017)Google Scholar
  17. 17.
    Scheible, C., Schütze, H.: Sentiment relevance. In: Proceedings of ACL 2013, vol. 1, pp. 954–963 (2013)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Bauman Moscow State Technical UniversityMoscowRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia

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