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Cascading Classifiers for Twitter Sentiment Analysis with Emotion Lexicons

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

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Abstract

Many different attempts have been made to determine sentiment polarity in tweets, using emotion lexicons and different NLP techniques with machine learning. In this paper we focus on using emotion lexicons and machine learning only, avoiding the use of additional NLP techniques. We present a scheme that is able to outperform other systems that use both natural language processing and distributional semantics. Our proposal consists on using a cascading classifier on lexicon features to improve accuracy. We evaluate our results with the TASS 2015 corpus, reaching an accuracy only 0.07 below the top-ranked system for task 1, 3 levels, whole test corpus. The cascading method we implemented consisted on using the results of a first stage classification with Multinomial Naïve Bayes as additional columns for a second stage classification using a Naïve Bayes Tree classifier with feature selection. We tested with at least 30 different classifiers and this combination yielded the best results.

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Notes

  1. 1.

    Our vectors can be downloaded at http://likufanele.com/twitterSEL as ARFF files.

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) LREC. European Language Resources Association, Paris (2010)

    Google Scholar 

  4. Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on interactive demonstrations, pp. 34–35. Association for Computational Linguistics (2005)

    Google Scholar 

  5. Stone, P.J.: The General Inquirer: A Computer Approach to Content Analysis. User’s Manual. MIT Press, Cambridge (1968)

    Google Scholar 

  6. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count: LIWC 20001, vol. 71. Lawrence Erlbaum Associates, Mahway (2001)

    Google Scholar 

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

    Article  Google Scholar 

  8. Villena Román, J., Lana Serrano, S., Martínez Cámara, E., González Cristóbal, J.C.: TASS-workshop on sentiment analysis at SEPLN (2013)

    Google Scholar 

  9. Villena-Román, J., García Morera, J., García-Cumbreras, M.Á., Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña López, L.A.: Overview of TASS 2015. In: TASS 2015: Workshop on Sentiment Analysis at SEPLN, vol. 1397. CEUR-WS.org (2015)

    Google Scholar 

  10. Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., Gatford, M., et al.: Okapi at TREC-3, P. 109. NIST Special Publication (1995)

    Google Scholar 

  11. Saralegi, X., San Vicente, I.: Elhuyar at TASS 2013. In: XXIX Congreso de la Sociedad Espaola de Procesamiento de lenguaje natural, Workshop on Sentiment Analysis at SEPLN (TASS 2013), pp. 143–150 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  13. Martınez-Cámara, E., Martın-Valdivia, M., Molina-González, M., Urena-López, L.: Bilingual experiments on an opinion comparable corpus. In: WASSA 2013, p. 87 (2013)

    Google Scholar 

  14. Molina-González, M.D., Martínez-Cámara, E., Martín-Valdivia, M.T., Perea-Ortega, J.M.: Semantic orientation for polarity classification in Spanish reviews. Expert Syst. Appl. 40, 7250–7257 (2013)

    Article  Google Scholar 

  15. Perez-Rosas, V., Banea, C., Mihalcea, R.: Learning sentiment lexicons in Spanish. In: LREC, vol. 12, p. 73(2012)

    Google Scholar 

  16. Rıos, M.G.D., Gravano, A.: Spanish DAL: a Spanish dictionary of affect in language. In: WASSA 2013, p. 21 (2013)

    Google Scholar 

  17. Redondo, J., Fraga, I., Padrón, I., Comesaña, M.: The Spanish adaptation of ANEW. Behav. Res. Methods 39, 600–605 (2007)

    Article  Google Scholar 

  18. Vilares, D., Doval, Y., Alonso, M.A., Gómez-Rodrıguez, C.: LyS at TASS 2014: a prototype for extracting and analysing aspects from Spanish tweets. In: Proceedings of the TASS workshop at SEPLN (2014)

    Google Scholar 

  19. Cruz, F.L., Troyano, J.A., Pontes, B., Ortega, F.J.: ML-SentiCon: un lexicón multilingüe de polaridades semánticas a nivel de lemas. Procesamiento del Leng. Nat. 53, 113–120 (2014)

    Google Scholar 

  20. Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, vol. 6, pp. 417–422 (2006)

    Google Scholar 

  21. Agerri, R., García-Serrano, A.: Q-WordNet: extracting polarity from WordNet senses. In: LREC (2010)

    Google Scholar 

  22. Manandhar, S., Yuret, D.: Second joint conference on lexical and computational semantics (*SEM). In: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2 (2013)

    Google Scholar 

  23. Deng, L., Wiebe, J.: MPQA 3.0: an entity/event-level sentiment corpus. In: Conference of the North American Chapter of the Association of Computational Linguistics: Human Language Technologies (2015)

    Google Scholar 

  24. Stone, P.J., Dunphy, D.C., Smith, M.S.: The general inquirer: a computer approach to content analysis (1966)

    Google Scholar 

  25. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: EMNLP, vol. 14, pp. 1532–1543 (2014)

    Google Scholar 

  26. Collins, M., Schapire, R.E., Singer, Y.: Logistic regression, Adaboost and Bregman distances. Mach. Learn. 48, 253–285 (2002)

    Article  MATH  Google Scholar 

  27. Martínez-Cámara, E., García-Cumbreras, M., Martín-Valdivia, M.T., Ureña López, L.A.: SINAI-EMMA: vectores de palabras para el análisis de opiniones en Twitter. In: TASS 2015: Workshop on Sentiment Analysis at SEPLN, vol. 1397. CEUR-WS.org (2015)

    Google Scholar 

  28. del Pilar Salas-Zárate, M., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., Alor-Hernández, G.: A study on LIWC categories for opinion mining in spanish reviews. J. Inf. Sci. 40, 749–760 (2014)

    Article  Google Scholar 

  29. Serendero, P., Toro, M.: Attribute selection for classification. In: Proceedings International Conference e-Society (IADIS)’, Lisbon, Portugal (2003)

    Google Scholar 

  30. Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)

    Google Scholar 

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

    Google Scholar 

  32. Dong, L., Frank, E., Kramer, S.: Ensembles of balanced nested dichotomies for multi-class problems. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 84–95. Springer, Heidelberg (2005). https://doi.org/10.1007/11564126_13

    Chapter  Google Scholar 

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Acknowledgments

We thank the support of Instituto Politécnico Nacional (IPN), ESCOM-IPN, SIP-IPN projects number 20160815, 20162058, COFAA-IPN, and EDI-IPN.

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Correspondence to Hiram Calvo .

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Calvo, H., Juárez Gambino, O. (2018). Cascading Classifiers for Twitter Sentiment Analysis with Emotion Lexicons. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-75487-1_21

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