Advertisement

Ontology-Enhanced Aspect-Based Sentiment Analysis

  • Kim SchoutenEmail author
  • Flavius Frasincar
  • Franciska de Jong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

With many people freely expressing their opinions and feelings on the Web, much research has gone into modeling and monetizing opinionated, and usually unstructured and textual, Web-based content. Aspect-based sentiment analysis aims to extract the fine-grained topics, or aspects, that people are talking about, together with the sentiment expressed on those aspects. This allows for a detailed analysis of the sentiment expressed in, for instance, product and service reviews. In this work we focus on knowledge-driven solutions that aim to complement standard machine learning methods. By encoding common domain knowledge into a knowledge repository, or ontology, we are able to exploit this information to improve classification performance for both aspect detection and aspect sentiment analysis. For aspect detection, the ontology-enhanced method needs only 20% of the training data to achieve results comparable with a standard bag-of-words approach that uses all training data.

Notes

Acknowledgments

The authors of this paper are supported by the Dutch national program COMMIT.

References

  1. 1.
    Cambria, E., Hussain, A.: Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Springer, Cham (2015)Google Scholar
  2. 2.
    Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), pp. 1515–1521. AAAI (2014)Google Scholar
  3. 3.
    Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)CrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm
  5. 5.
    Dragoni, M., Tettamanzi, A.G.B., Costa Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., Stankovic, M., Cambria, E., Cantador, I., Iorio, A., Noia, T., Lange, C., Reforgiato Recupero, D., Tordai, A. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 21–27. Springer, Cham (2014). doi: 10.1007/978-3-319-12024-9_2 Google Scholar
  6. 6.
    Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), vol. 6, pp. 417–422. European Language Resources Association (ELRA) (2006)Google Scholar
  7. 7.
    Fellbaum, C.: WordNet: an Electronic Lexical Database. MIT Press (1998)Google Scholar
  8. 8.
    Guarino, N., Welty, C.: Evaluating ontological decisions with OntoClean. Commun. ACM 45(2), 61–65 (2002)CrossRefGoogle Scholar
  9. 9.
    Jensen, A., Boss, N.: Textual Similarity: Comparing Texts in Order to Discover How Closely They Discuss the Same Topics. Bsc. thesis, Technical University of Denmark (2008). http://etd.dtu.dk/thesis/220969/bac08_15.pdf
  10. 10.
  11. 11.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1746–1751. The Association for Computer Linguistics (2014)Google Scholar
  12. 12.
    Kiritchenko, S., Zhu, X., Cherry, C., Mohammad, S.: NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 437–442. Association for Computational Linguistics and Dublin City University (2014)Google Scholar
  13. 13.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation (SIGDOC 1986), pp. 24–26. ACM (1986)Google Scholar
  14. 14.
    Liu, B.: Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies, vol. 16. Morgan & Claypool (2012)Google Scholar
  15. 15.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60. Association for Computational Linguistics (2014)Google Scholar
  16. 16.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  17. 17.
    Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the Ninth International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495. Association for Computational Linguistics (2015)Google Scholar
  18. 18.
    Recupero Reforgiate, D., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: frame-based sentiment analysis. Cogn. Comput. 7(2), 211–225 (2015)Google Scholar
  19. 19.
    Saias, J.: Sentiue: target and aspect based sentiment analysis in SemEval-2015 task 12. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 767–771. Association for Computational Linguistics (2015)Google Scholar
  20. 20.
    Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2016)CrossRefGoogle Scholar
  21. 21.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.P.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods on Natural Language Processing (EMNLP 2013), pp. 1631–1642. Association for Computational Linguistics (2013)Google Scholar
  22. 22.
    Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL 2015), pp. 1556–1566. The Association for Computer Linguistics (2015)Google Scholar
  23. 23.
    Toh, Z., Su, J.: NLANGP: supervised machine learning system for aspect category classification and opinion target extraction. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 496–501. Association for Computational Linguistics (2015)Google Scholar
  24. 24.
    Xia, Y., Cambria, E., Hussain, A., Zhao, H.: Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features. Cogn. Comput. 7(3), 369–380 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kim Schouten
    • 1
    Email author
  • Flavius Frasincar
    • 1
  • Franciska de Jong
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

Personalised recommendations