Skip to main content

An Approach to Sentiment Analysis of Movie Reviews: Lexicon Based vs. Classification

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

Included in the following conference series:

Abstract

The paper examines two approaches to sentiment analysis: lexicon-based vs. supervised learning in the domain of movie reviews. In evaluation, the methods were compared using a standard movie review test collection. The results show that lexicon-based approach is easily outperformed by classification approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Diz, M.L.B., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. Int. J. Neural Syst. 21(4), 277–296 (2011)

    Article  Google Scholar 

  2. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 347–354. Association for Computational Linguistics, Stroudsburg (2005)

    Chapter  Google Scholar 

  3. Esuli, A.: The user feedback on sentiwordnet. CoRR abs/1306.1343 (2013)

    Google Scholar 

  4. Huang, S., Niu, Z., Shi, C.: Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl.-Based Syst. 56, 191–200 (2014)

    Article  Google Scholar 

  5. Cruz, F.L., Troyano, J.A., Ortega, F.J., Enríquez, F.: Automatic expansion of feature-level opinion lexicons. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, WASSA 2011, pp. 125–131. Association for Computational Linguistics, Stroudsburg (2011)

    Google Scholar 

  6. 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, ACL 2002, pp. 417–424. Association for Computational Linguistics, Stroudsburg (2002)

    Google Scholar 

  7. Thet, T.T., Na, J.C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 823–848 (2010)

    Article  Google Scholar 

  8. 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, ACL 2004. Association for Computational Linguistics, Stroudsburg (2004)

    Google Scholar 

  9. Basari, A.S.H., Hussin, B., Ananta, I.G.P., Zeniarja, J.: Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Engineering 53, 453–462 (2013)

    Article  Google Scholar 

  10. Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of ACL, pp. 115–124 (2005)

    Google Scholar 

  11. Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, ACL 1998, pp. 174–181. Association for Computational Linguistics, Stroudsburg (1997)

    Google Scholar 

  12. Turney, P., Littman, M.: Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical report nrc technical report erb-1094, Institute for Information Technology, National Research Council Canada (2002)

    Google Scholar 

  13. Gamon, M., Aue, A.: Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms. In: Proceedings of the ACL 2005 Workshop on Feature Engineering for Machine Learning in NLP, ACL, pp. 57–64 (2005)

    Google Scholar 

  14. Steinberger, J., Ebrahim, M., Ehrmann, M., Hurriyetoglu, A., Kabadjov, M., Lenkova, P., Steinberger, R., Tanev, H., Vázquez, S., Zavarella, V.: Creating sentiment dictionaries via triangulation. Decision Support Systems 53(4), 689–694 (2012) (Computational Approaches to Subjectivity and Sentiment Analysis 2) Service Science in Information Systems Research: Special Issue on {PACIS} (2010)

    Google Scholar 

  15. Mihalcea, R., Banea, C., Wiebe, J.: Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 976–983. Association for Computational Linguistics, Prague (2007)

    Google Scholar 

  16. Shi, L., Mihalcea, R., Tian, M.: Cross language text classification by model translation and semi-supervised learning. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 1057–1067. Association for Computational Linguistics, Stroudsburg (2010)

    Google Scholar 

  17. Min Kim, S.: Determining the sentiment of opinions. In: Proceedings of COLING, pp. 1367–1373 (2004)

    Google Scholar 

  18. Kamps, J., Marx, M., Mokken, R.J., Rijke, M.D.: Using wordnet to measure semantic orientation of adjectives. In: National Institute for, pp. 1115–1118 (2004)

    Google Scholar 

  19. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177. ACM, New York (2004)

    Chapter  Google Scholar 

  20. Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM 2005, pp. 617–624. ACM, New York (2005)

    Google Scholar 

  21. Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers (2012)

    Google Scholar 

  22. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learningtechniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods Innatural Language Processing, EMNLP 2002, vol. 10, pp. 79–86. Association for Computational Linguistics, Stroudsburg (2002)

    Google Scholar 

  23. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)

    Article  Google Scholar 

  24. Ortigosa-Hernández, J., Rodríguez, J.D., Alzate, L., Lucania, M., Inza, I., Lozano, J.A.: Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92, 98–115 (2012)

    Article  Google Scholar 

  25. Ohsawa, Y., Benson, N.E., Yachida, M.: Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In: ADL 1998: Proceedings of the Advances in Digital Libraries Conference, p. 12. IEEE Computer Society, Washington, DC (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Augustyniak, L., Kajdanowicz, T., Kazienko, P., Kulisiewicz, M., Tuliglowicz, W. (2014). An Approach to Sentiment Analysis of Movie Reviews: Lexicon Based vs. Classification. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07617-1_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics