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Searching for the Most Negative Opinions

  • Sattam AlmatarnehEmail author
  • Pablo Gamallo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 786)

Abstract

Studies in sentiment analysis and opinion mining have been focused on several aspects of opinions, such as their automatic extraction, identification of their polarity (positive, negative or neutral), the entities or facets involved, and so on. However, to the best of our knowledge, no sentiment analysis approach has considered the automatic identification and extraction of the most negative opinions, in spite of their significant impact in many fields such as industry, trade, political and socials issues.

In this article, we will use diversified linguistic features and supervised machine learning algorithms so as to examine their effectiveness in the process of searching for the most negative opinions.

Keywords

Sentiment analysis Opinion mining Linguistic features Classification Most negative opinion 

References

  1. 1.
    Agarwal, B., Mittal, N.: Prominent Feature Extraction for Sentiment Analysis. Socio-Affective Computing. Springer, Cham (2016). doi: 10.1007/978-3-319-25343-5 CrossRefGoogle Scholar
  2. 2.
    Almatarneh, S., Gamallo, P.: Automatic construction of domain-specific sentiment lexicons for polarity classification. In: De la Prieta, F., Vale, Z., Antunes, L., Pinto, T., Campbell, A.T., Julián, V., Neves, A.J.R., Moreno, M.N. (eds.) PAAMS 2017. AISC, vol. 619, pp. 175–182. Springer, Cham (2018). doi: 10.1007/978-3-319-61578-3_17 CrossRefGoogle Scholar
  3. 3.
    Benamara, F., Taboada, M., Mathieu, Y.: Evaluative language beyond bags of words: linguistic insights and computational applications. Comput. Linguist. 43, 201–264 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)CrossRefGoogle Scholar
  5. 5.
    Chenlo, J.M., Losada, D.E.: An empirical study of sentence features for subjectivity and polarity classification. Inf. Sci. 280, 275–288 (2014)CrossRefGoogle Scholar
  6. 6.
    Chevalier, J.A., Mayzlin, D.: The effect of word of mouth on sales: online book reviews. J. Mark. Res. 43(3), 345–354 (2006)CrossRefGoogle Scholar
  7. 7.
    Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240. ACM (2008)Google Scholar
  8. 8.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics (1997)Google Scholar
  9. 9.
    Heerschop, B., Goossen, F., Hogenboom, A., Frasincar, F., Kaymak, U., de Jong, F.: Polarity analysis of texts using discourse structure. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1061–1070. ACM (2011)Google Scholar
  10. 10.
    Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)Google Scholar
  11. 11.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  12. 12.
    Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)Google Scholar
  13. 13.
    Kamps, J., Marx, M., Mokken, R.J., De Rijke, M., et al.: Using wordnet to measure semantic orientations of adjectives. In: LREC, vol. 4, pp. 1115–1118 (2004)Google Scholar
  14. 14.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  18. 18.
    Papathanassis, A., Knolle, F.: Exploring the adoption and processing of online holiday reviews: a grounded theory approach. Tourism Manag. 32(2), 215–224 (2011)CrossRefGoogle Scholar
  19. 19.
    Parapar, J., Losada, D.E., Barreiro, A.: A learning-based approach for the identification of sexual predators in chat logs. In: CLEF (Online Working Notes/Labs/Workshop) (2012)Google Scholar
  20. 20.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC2015. Technical report (2015)Google Scholar
  22. 22.
    Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of web services. Inf. Sci. 311, 18–38 (2015)CrossRefGoogle Scholar
  23. 23.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  24. 24.
    Takamura, H., Inui, T., Okumura, M.: Extracting semantic orientations of phrases from dictionary. In: HLT-NAACL, vol. 2007, pp. 292–299 (2007)Google Scholar
  25. 25.
    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, pp. 417–424. Association for Computational Linguistics (2002)Google Scholar
  26. 26.
    Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 129–136. Association for Computational Linguistics (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centro Singular de Investigación en Tecnoloxías da Información (CITIUS)Universidad de Santiago de CompostelaSantiago de CompostelaSpain

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