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Linguistic Features to Identify Extreme Opinions: An Empirical Study

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Intelligent Data Engineering and Automated Learning – IDEAL 2018 (IDEAL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11314))

Abstract

Studies in sentiment analysis and opinion mining have examined how different features are effective in polarity classification by making use of positive, negative or neutral values. However, the identification of extreme opinions (most negative and most positive opinions) have overlooked in spite of their wide significance in many applications. In our study, we will combine empirical features (e.g. bag of words, word embeddings, polarity lexicons, and set of textual features) so as to identify extreme opinions and provide a comprehensive analysis of the relative importance of each set of features using hotel reviews.

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Notes

  1. 1.

    http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html.

  2. 2.

    https://radimrehurek.com/gensim/.

  3. 3.

    https://github.com/citiususc/VERY-NEG-and-VERY-POS-Lexicons.

  4. 4.

    http://www.stanford.edu/~cgpotts/data/wordnetscales/.

  5. 5.

    http://ave.dee.isep.ipp.pt/~1080560/ExpediaDataSet.7z.

  6. 6.

    http://scikit-learn.org/stable/.

References

  1. Almatarneh, S., Gamallo, P.: Automatic construction of domain-specific sentiment lexicons for polarity classification. In: De la Prieta, F., et al. (eds.) PAAMS 2017. AISC, vol. 619, pp. 175–182. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_17

    Chapter  Google Scholar 

  2. Almatarneh, S., Gamallo, P.: Searching for the most negative opinions. In: Różewski, P., Lange, C. (eds.) KESW 2017. CCIS, vol. 786, pp. 14–22. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69548-8_2

    Chapter  Google Scholar 

  3. Almatarneh, S., Gamallo, P.: A lexicon based method to search for extreme opinions. PloS ONE 13(5), e0197816 (2018)

    Article  Google Scholar 

  4. Chenlo, J.M., Losada, D.E.: An empirical study of sentence features for subjectivity and polarity classification. Inf. Sci. 280, 275–288 (2014)

    Article  Google Scholar 

  5. Dai, A.M., Olah, C., Le, Q.V.: Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015)

  6. Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006)

    Article  MathSciNet  Google Scholar 

  7. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

    Google Scholar 

  8. Lee, J., Park, D.H., Han, I.: The effect of negative online consumer reviews on product attitude: an information processing view. Electron. Commer. Res. Appl. 7(3), 341–352 (2008)

    Article  Google Scholar 

  9. Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)

    Book  Google Scholar 

  10. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  11. Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 115–124. Association for Computational Linguistics (2005)

    Google Scholar 

  12. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  13. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  14. Potts, C.: On the negativity of negation. Semant. Linguist. Theory 20, 636–659 (2010)

    Article  Google Scholar 

  15. Potts, C.: Developing adjective scales from user-supplied textual metadata. In: NSF Workshop on Restructuring Adjectives in WordNet, Arlington, VA (2011)

    Google Scholar 

  16. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  17. Tripathy, A., Agrawal, A., Rath, S.K.: Classification of sentiment reviews using n-gram machine learning approach. Expert Syst. Appl. 57, 117–126 (2016)

    Article  Google Scholar 

  18. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49. ACM (1999)

    Google Scholar 

  19. Zhang, Z., Ye, Q., Zhang, Z., Li, Y.: Sentiment classification of internet restaurant reviews written in Cantonese. Expert Syst. Appl. 38(6), 7674–7682 (2011)

    Article  Google Scholar 

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Acknowledgements

This work has received financial support from TelePares (MINECO, ref:FFI201 4-51978-C2-1-R), and the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08) and the European Regional Development Fund (ERDF).

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), Universidad de Santiago de Compostela, Rua de Jenaro de la Fuente Domínguez, 15782, Santiago de Compostela, Spain

    Sattam Almatarneh & Pablo Gamallo

Authors
  1. Sattam Almatarneh
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  2. Pablo Gamallo
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Corresponding author

Correspondence to Sattam Almatarneh .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Almatarneh, S., Gamallo, P. (2018). Linguistic Features to Identify Extreme Opinions: An Empirical Study. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-03493-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03492-4

  • Online ISBN: 978-3-030-03493-1

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