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Analysis of Negation Cues for Semantic Orientation Classification of Reviews in Spanish

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9414))

Abstract

We study the effect of negation cues on semantic orientation prediction. State-of-the-art approaches to semantic orientation derivation are based on automatic classification. We analyze the use of negation cues as features for both supervised and unsupervised methods. We apply such methods on a collection of washing-machine reviews in Spanish. We compare the results of the two approaches and discuss the performance of each negation cue. We found that simple features performed similarly to using more resources.

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Notes

  1. 1.

    http://sentiwordnet.isti.cnr.it/, lexical resource for opinion mining that assigns three sentiment scores: positivity, negativity and objectivity.

  2. 2.

    http://www.sfu.ca/~mtaboada/research/SFU_Review_Corpus.html.

  3. 3.

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

References

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

    Article  Google Scholar 

  2. Liu, B.: Sentiment analysis and subjectivity. Handb. Nat. Lang. Process. 2, 627–666 (2010)

    Google Scholar 

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

    Google Scholar 

  4. Wiegand, M., Balahur, A., Roth, B., Klakow, D., Montoyo, A.: A survey on the role of negation in sentiment analysis. In: Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, pp. 60–68 (2010)

    Google Scholar 

  5. Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference of the European Chapter of the Association for Computational Linguistics, EACL 1997, pp. 174–181 (1997)

    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 of the Association for Computational Linguistics, pp. 417–424 (2002)

    Google Scholar 

  7. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86 (2002)

    Google Scholar 

  8. Poria, S., Cambria, E., Gelbukh, A.: Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: Proceedings of EMNLP 2015, Lisbon, pp. 2539–2544 (2015)

    Google Scholar 

  9. Poria, S., Gelbukh, A., Hussain, A., Howard, N., Das, D., Bandyopadhyay, S.: Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intell. Syst. 28(2), 31–38 (2013)

    Article  Google Scholar 

  10. Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., Hussain, A.: Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn. Comput. 7(4), 487–499 (2015)

    Article  Google Scholar 

  11. Cambria, E., Fu, J., Bisio, F., Poria, S.: AffectiveSpace 2: enabling affective intuition for concept-level sentiment analysis. In: Proceedings of Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 508–514 (2015)

    Google Scholar 

  12. Poria, S., Cambria, E., Hussain, A., Huang, G.-B.: Towards an intelligent framework for multimodal affective data analysis. Neural Netw. 63, 104–116 (2015)

    Article  Google Scholar 

  13. Poria, S., Cambria, E., Howard, N., Huang, G.-B., Hussain, A.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing (2015, in press). doi:10.1016/j.neucom.2015.01.095

  14. Cambria, E., Poria, S., Bisio, F., Bajpai, R., Chaturvedi, I.: The CLSA model: a novel framework for concept-level sentiment analysis. In: Gelbukh, A. (ed.). LNCS, vol. 9042, pp. 3–22. Springer, Heidelberg (2015)

    Google Scholar 

  15. Poria, S., Gelbukh, A., Cambria, E., Hussain, A., Huang, G.-B.: EmoSenticSpace: a novel framework for affective common-sense reasoning. Knowl.-Based Syst. 69, 108–123 (2014)

    Article  Google Scholar 

  16. Martín-Valdivia, M.T., Martínez-Cámara, E., Perea-Ortega, J.M., Ureña López, L.A.: Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches. Expert Syst. Appl. 40, 3934–3942 (2013)

    Article  Google Scholar 

  17. Cruz Mata, F., Troyano Jiménez, J.A., de Salamanca Ros, F.E., Rodríguez, F.J.O.: Clasificación de documentos basada en la opinión: experimentos con un corpus de críticas de cine en español. Procesamiento del lenguaje natural 41, 73–80 (2008)

    Google Scholar 

  18. Vilares, D., Alonso, M.A., Gómez-Rodríguez, C.: A syntactic approach for opinion mining on Spanish reviews. Nat. Lang. Eng. 1(1), 1–26 (2013)

    Google Scholar 

  19. Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94 (2012)

    Google Scholar 

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

    Google Scholar 

  21. 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, pp. 793–801 (2008)

    Google Scholar 

  22. Poria, S., Cambria, E., Winterstein, G., Huang, G.-B.: Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl.-Based Syst. 69, 45–63 (2014)

    Article  Google Scholar 

  23. Poria, S., Cambria, E., Gelbukh, A., Bisio, F., Hussain, A.: Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Comput. Intell. Mag. 10(4), 26–36 (2015)

    Article  Google Scholar 

  24. Chikersal, P., Poria, S., Cambria, E.: SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the International Workshop on Semantic Evaluation, SemEval 2015, pp. 647–651 (2015)

    Google Scholar 

  25. Chikersal, P., Poria, S., Cambria, E., Gelbukh, A., Siong, C.E.: Modelling public sentiment in Twitter: using linguistic patterns to enhance supervised learning. In: Gelbukh, A. (ed.). LNCS, vol. 9042, pp. 49–65. Springer, Heidelberg (2015)

    Google Scholar 

  26. Jiménez Zafra, S.M., Cámara, E.M., Valdivia, M.T.M., González, M.D.M.: Tratamiento de la negación en el análisis de opiniones en español. Procesamiento del Lenguaje Natural 54, 37–44 (2015)

    Google Scholar 

  27. Díaz-Rangel, I., Sidorov, G., Suárez-Guerra, S.: Creación y evaluación de un diccionario marcado con emociones y ponderado para el español. Onomazein 29, 31–46 (2014)

    Google Scholar 

  28. Galicia-Haro, S.N., Gelbukh, A.: Extraction of semantic relations from opinion reviews in Spanish. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) MICAI 2014, Part I. LNCS, vol. 8856, pp. 175–190. Springer, Heidelberg (2014)

    Google Scholar 

  29. Padró, L., Stanilovsky, E.: Freeling 3.0: towards wider multilinguality. In: Proceedings of the Language Resources and Evaluation Conference (LREC 2012), Istanbul, Turkey, pp. 2473–2479. ELRA (2012)

    Google Scholar 

  30. López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)

    Article  Google Scholar 

  31. Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007)

    Article  MATH  Google Scholar 

  32. Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C.: Reducing misclassification costs. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 217–225 (1994)

    Google Scholar 

  33. Tang, Y., Zhang, Y.-Q., Chawla, N.V., Krasser, S.: Svms modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 281–288 (2009)

    Article  Google Scholar 

  34. Akbani, R., Kwek, S.S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  35. Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Folleco, A.: An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Inf. Sci. 259, 571–595 (2014)

    Article  Google Scholar 

  36. Sidorov, G., Miranda-Jiménez, S., Viveros-Jiménez, F., Gelbukh, A., Castro-Sánchez, N., Velásquez, F., Díaz-Rangel, I., Suárez-Guerra, S., Treviño, A., Gordon, J.: Empirical study of machine learning based approach for opinion mining in Tweets. In: Batyrshin, I., González Mendoza, M. (eds.) MICAI 2012, Part I. LNCS, vol. 7629, pp. 1–14. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  37. Spitzová, E.: Sintaxis de la lengua española. Masarykova Univerzita, Brno (1994)

    Google Scholar 

  38. Blanco, E., Moldovan, D.: Semantic representation of negation using focus detection. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), pp. 581–589 (2011)

    Google Scholar 

  39. Sanz Alonso, B.: La negación en español. In: Actuales tendencias en la enseñanza del español como lengua extranjera II: actas del VI Congreso Internacional de ASELE. pp. 379–384 (1996)

    Google Scholar 

  40. Bergareche, B.C.: Negación doble y negación simple en español moderno. Revista de filología románica (9), 63–102 (1992)

    Google Scholar 

  41. Manning, C.D., Raghavan, P., Schuetze, H.: Information Retrieval. Cambridge University Press, Cambridge (2008)

    MATH  Google Scholar 

  42. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  43. Poria, S., Gelbukh, A., Agarwal, B., Cambria, E., Howard, N.: Common sense knowledge based personality recognition from text. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013, Part II. LNCS, vol. 8266, pp. 484–496. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  44. Pakray, P., Poria, S., Bandyopadhyay, S., Gelbukh, A.: Semantic textual entailment recognition using UNL. Polibits 43, 23–27 (2011)

    Article  Google Scholar 

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Acknowledgments

The fourth author recognizes the support of the Instituto Politécnico Nacional, grants SIP 20152095 and SIP 20152100.

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Correspondence to Sofía N. Galicia-Haro .

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Galicia-Haro, S.N., Palomino-Garibay, A., Gallegos-Acosta, J., Gelbukh, A. (2015). Analysis of Negation Cues for Semantic Orientation Classification of Reviews in Spanish. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-27101-9_8

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