Advertisement

An Information Gain-Driven Feature Study for Aspect-Based Sentiment Analysis

  • Kim SchoutenEmail author
  • Flavius Frasincar
  • Rommert Dekker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9612)

Abstract

Nowadays, opinions are a ubiquitous part of the Web and sharing experiences has never been more popular. Information regarding consumer opinions is valuable for consumers and producers alike, aiding in their respective decision processes. Due to the size and heterogeneity of this type of information, computer algorithms are employed to gain the required insight. Current research, however, tends to forgo a rigorous analysis of the used features, only going so far as to analyze complete feature sets. In this paper we analyze which features are good predictors for aspect-level sentiment using Information Gain and why this is the case. We also present an extensive set of features and show that it is possible to use only a small fraction of the features at just a minor cost to accuracy.

Keywords

Sentiment analysis Aspect-level sentiment analysis Data mining Feature analysis Feature selection Information gain 

Notes

Acknowledgments

The authors are supported by the Dutch national program COMMIT. We would like to thank Nienke Dijkstra, Vivian Hinfelaar, Isabelle Houck, Tim van den IJssel, and Eline van de Ven, for many fruitful discussions during this research.

References

  1. 1.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), vol. 10, pp. 2200–2204 (2010)Google Scholar
  2. 2.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)CrossRefGoogle Scholar
  3. 3.
    Choi, Y., Cardie, C.: Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing 2008 (EMNLP 2008), pp. 793–801 (2008)Google Scholar
  4. 4.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)CrossRefGoogle Scholar
  5. 5.
    Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26–32 (2013)CrossRefGoogle Scholar
  6. 6.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 168–177. ACM (2004)Google Scholar
  7. 7.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the Fifth Annual International Conference on Systems Documentation (SIGDOC 1986), pp. 24–26. ACM (1986)Google Scholar
  8. 8.
    Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, San Rafael (2012)Google Scholar
  9. 9.
    Mann, W.C., Thompson, S.A.: Rhetorical structure theory: toward a functional theory of text organization. Text Interdiscip. J. Study Discourse 8(3), 243–281 (1988)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Mitchell, T.M.: Machine Learning, 1st edn. McGraw-Hill Inc., New York (1997)zbMATHGoogle Scholar
  12. 12.
    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), pp. 486–495. Association for Computational Linguistics (2015)Google Scholar
  13. 13.
    Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2016)CrossRefGoogle Scholar
  14. 14.
    Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT 2008), pp. 308–316. ACL (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kim Schouten
    • 1
    Email author
  • Flavius Frasincar
    • 1
  • Rommert Dekker
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

Personalised recommendations