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The Benefit of Concept-Based Features for Sentiment Analysis

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Semantic Web Evaluation Challenges (SemWebEval 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 548))

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Abstract

Sentiment analysis is an active field of research, moving from the traditional algorithms that operated on complete documents to fine-grained variants where aspects of the topic being discussed are extracted, as well as their associated sentiment. Recently, a move from traditional word-based approaches to concept-based approaches has started. In this work, it is shown by using a simple machine learning baseline, that concepts are useful as features within a machine learning framework. In all our experiments, the performance increases when including the concept-based features.

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References

  1. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th Language Resources and Evaluation Conference (LREC 2010), pp. 2200–2204 (2010)

    Google Scholar 

  2. Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In: Proceedings of the Association for Computational Linguistics, pp. 187–205. ACL (2007)

    Google Scholar 

  3. Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of AAAI 2014, pp. 1515–1521 (2014)

    Google Scholar 

  4. Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intel. Syst. 28(2), 15–21 (2013)

    Article  Google Scholar 

  5. Cesarano, C., Dorr, B., Picariello, A., Reforgiato, D., Sagoff, A., Subrahmanian, V.: Oasys: An opinion analysis system. In: AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs (2004)

    Google Scholar 

  6. Chung, J.K.-C., Wu, C.-E., Tsai, R.T.-H.: Polarity detection of online reviews using sentiment concepts: NCU IISR team at ESWC-14 challenge on concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 53–58. Springer, Heidelberg (2014)

    Google Scholar 

  7. Coden, A., Gruhl, D., Lewis, N., Mendes, P.N., Nagarajan, M., Ramakrishnan, C., Welch, S.: Semantic lexicon expansion for concept-based aspect-aware sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 34–40. Springer, Heidelberg (2014)

    Google Scholar 

  8. Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 21–27. Springer, Heidelberg (2014)

    Google Scholar 

  9. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  10. 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 (ACL1998), pp. 174–181. Association for Computational Linguistics (1997). http://dx.doi.org/10.3115/976909.979640

  11. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6(2), 167–195 (2015)

    Google Scholar 

  12. Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: 5th Annual International Conference on Systems Documentation (SIGDOC 1986), pp. 24–26. ACM (1986)

    Google Scholar 

  13. Mendes, P.N., Jakob, M., Bizer, C.: DBpedia: A Multilingual cross-domain knowledge base. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012), pp. 1813–1817 (2012)

    Google Scholar 

  14. Ofek, N., Rokach, L.: Unsupervised fine-grained sentiment analysis system using lexicons and concepts. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 28–33. Springer, Heidelberg (2014)

    Google Scholar 

  15. Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodríguez-García, Á., Moreno, V., Sánchez-Cervantes, J.L.: Feature-based opinion mining through ontologies. Expert Syst. Appl. 41(13), 5995–6008 (2014)

    Article  Google Scholar 

  16. Pontiki, M., Galanis, D., Papageogiou, 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) (2015)

    Google Scholar 

  17. Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 41–47. Springer, Heidelberg (2014)

    Google Scholar 

  18. Quan, C., Ren, F.: A blog emotion corpus for emotional expression analysis in chinese. Comput. Speech Lang. 24(4), 726–749 (2010)

    Article  Google Scholar 

  19. Rajagopal, D., Cambria, E., Olsher, D., Kwok, K.: A graph-based approach to commonsense concept extraction and semantic similarity detection. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW 2013 Companion, pp. 565–570. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  20. Schouten, K., Frasincar, F., de Jong, F.: Commit-p1wp3: A co-occurrence based approach to aspect-level sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 203–207. Association for Computational Linguistics and Dublin City University (2014)

    Google Scholar 

  21. Shi, W., Wang, H., He, S.: Sentiment analysis of chinese microblogging based on sentiment ontology: a case study of ‘7.23 wenzhou train collision’. Connection Sci. 25(4), 161–178 (2013)

    Article  MathSciNet  Google Scholar 

  22. Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press (1966)

    Google Scholar 

  23. Tsai, A.R., Wu, C.E., Tsai, R.H., Hsu, J.: Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intel. Syst. 28(2), 22–30 (2013)

    Article  Google Scholar 

  24. Virk, S.M., Lee, Y.-H., Ku, L.-W.: Sinica Semantic Parser for ESWC’14 Concept-Level Semantic Analysis Challenge. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 48–52. Springer, Heidelberg (2014)

    Google Scholar 

  25. Yin, P., Wang, H., Guo, K.: Feature-opinion pair identification of product reviews in chinese: a domain ontology modeling method. New Rev. Hypermedia multimedia 19(1), 3–24 (2013)

    Article  Google Scholar 

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Acknowledgment

The authors are partially supported by the Dutch national program COMMIT.

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Authors and Affiliations

  1. Erasmus University Rotterdam, PO Box 1738, 3000 DR, Rotterdam, The Netherlands

    Kim Schouten & Flavius Frasincar

Authors
  1. Kim Schouten
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  2. Flavius Frasincar
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Corresponding author

Correspondence to Kim Schouten .

Editor information

Editors and Affiliations

  1. Inria, Sophia Antipolis, France

    Fabien Gandon

  2. INRIA Sophia-Antipolis Méditerranée, Sophia Antipolis, France

    Elena Cabrio

  3. Université Paris-Sorbonne, Paris, France

    Milan Stankovic

  4. École des Mines de Saint-Étienne, Saint-Étienne, France

    Antoine Zimmermann

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Schouten, K., Frasincar, F. (2015). The Benefit of Concept-Based Features for Sentiment Analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-25518-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25517-0

  • Online ISBN: 978-3-319-25518-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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