¿Tiene carácter predictivo la estructura predicativa [verbo + objeto directo]? Hacia una caracterización sintáctico-semántica para propósitos de análisis de sentimientos

Carlos Mario Pérez Pérez; Gabriel Ángel Quiroz Herrera; Antonio Jesús Tamayo Herrera

doi:10.17533/udea.lyl.n78a01

Authors

Carlos Mario Pérez Pérez University of Antioquia
Gabriel Ángel Quiroz Herrera University of Antioquia
Antonio Jesús Tamayo Herrera University of Antioquia

DOI:

https://doi.org/10.17533/udea.lyl.n78a01

Keywords:

sentiment analysis, verb, direct object complement, poverty in Colombia, corpus linguistics

Abstract

Traditionally, sentiment analysis has focused on processing less complex textual units, as sentences, through detailed characterization of their components like adjectives. However, there are other types of units, such as predicative structures, that might be discriminant elements of more complex discursive units like the news. Thus, in this article, word occurrences of the predicative structure [verb + direct object] are extracted and characterized from a corpus on poverty compiled from Colombian newspapers. The results demonstrate that these units are discriminant and might help with polarity classification.

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Author Biographies

Carlos Mario Pérez Pérez, University of Antioquia

Master in Linguistics from the University of Antioquia (Colombia). Professor and member of the research group in Translation and New Technologies-TNT, University of Antioquia (Colombia). Candidate for Doctor of Linguistics of the Faculty of Communications of the same University. He develops his teaching and research in the areas of translation, terminology, linguistics and language technologies.

Gabriel Ángel Quiroz Herrera, University of Antioquia

Ph.D. in Applied Linguistics from Pompeu Fabra University (Spain). Professor at the School of Languages and member of the research group in Translation and New Technologies-TNT at University of Antioquia (Colombia). He develops his teaching and research in the areas of translation, terminology and language technologies.

Antonio Jesús Tamayo Herrera, University of Antioquia

Master in Engineering at the University of Antioquia (Colombia). Professor of Systems Engineering and member of the research group in Translation and New Technologies-TNT at University of Antioquia (Colombia). Candidate for Doctor of Computer Science from the Center for Computer Research of the National Polytechnic Institute (Mexico). He develops his teaching and research in the areas of natural language processing, machine learning, statistics and computational linguistics.

References

Alcoba, S. (1999). La flexión verbal. En Bosque, I., & Demonte, V. (Eds.). Gramática Descriptiva de la Lengua Española (p. 4917).

Álvarez, S., & Naharro, N. (2017). Representaciones de la pobreza en la prensa hegemónica Argentina. En Chiquito, A., & Quiroz, G. (Eds.). Pobreza, Lenguaje y Medios en América Latina (pp. 235–264). Berna: Peter Lang.

Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., & Subrahmanian, V. (2007). Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone. En Proceedings of International Conference on Weblogs and Social Media.

Bosque, I. (2006). Diccionario combinatorio práctico del español contemporáneo. Madrid: Ediciones SM.

Chiquito, A., & Quiroz, G. (Eds.) (2017). Pobreza, lenguaje y medios en América Latina. Berna: Peter Lang.

Chomsky, N. (1997). What Makes Mainstream Media Mainstream. Recuperado de http://www.zmag.org/chomsky/articles/z9710-mainstream-media.html

Colombia. DANE. (2012). Pobreza en Colombia. Bogotá D.C.: Oficina de Prensa DANE.

Ding, X., Liu, B., & Yu, P. S. (2008). A Holistic Lexicon-based Approach to Opinion Mining. En Proceedings of the International Conference on Web Search and Web Data Mining - WSDM’08 (pp. 231-240).

Gili Gaya, S. (1980). Curso superior de sintaxis española. Barcelona: Vox Biblograf.

Hatzivassiloglou, V. & McKeown, K. R. (1997). Predicting the Semantic Orientation of Adjectives. En Proceedings of the 35th Annual Meeting on Association for Computational Linguistics (pp. 174–181). Association for Computational Linguistics.

Hatzivassiloglou, V. & Wiebe, J. M. (2000). Effects of Adjective Orientation and Gradability on Sentence Subjectivity. En Proceedings of the 18th Conference on Computational Linguistics, (1), (pp. 299-305). Association for Computational Linguistics.

Joshi, M. & Penstein-Rosé, C. (2009). Generalizing Dependency Features for Opinion Mining. En Proceedings of the ACL-IJCNLP 2009 Conference Short Papers (pp. 313-316).

Koppel, M. & Schler, J. (2006). The Importance of Neutral Examples for Learning Sentiment. Computational Intelligence, 22(2), pp. 100-109.

Levin, B. (1993). English Verb Classes and Alternations. Chicago: The University of Chicago Press.

Liu, B. (2012). Sentiment Analysis and Opinion Mining. En Hirst, G. (Ed.). Synthesis Lectures on Human Language Technologies (pp. 1-167). California: Morgan & Claypool.

Lorente, M. (2002). Verbos y discurso especializado. Estudios de Lingüística del Español [En línea]. Recuperado de http://elies.rediris.es/elies16/Lorente.html

Mullen, T., & Collier, N. (2004). Sentiment Analysis Using Support Vector Machines with Diverse Information Sources. En Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (4), (pp. 412-418).

Nasukawa, T., & Yi, J. (2003). Sentiment Analysis: Capturing Favorability Using Natural Language Processing. En Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77).

Naciones Unidas. (2015). Objetivos de Desarrollo del Milenio. Nuevas Ediciones S.A. Recuperado de http://www.un.org/es/millenniumgoals/pdf/PR_Global_MDG09_SP.pdf

Ng, V., Dasgupta, S., & Arifin, S. (2006). Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews. En Proceedings of the COLING/ACL on Main Conference Poster Sessions (pp. 611-618).

Pardo, N. & Ruiz, J. (2017). Pobreza y bienestar en Colombia, construcción de referenciales en perspectiva mediática. En Chiquito, A. & Quiroz, G. (Eds.). Pobreza, lenguaje y medios en América Latina (pp. 265-302). Berna: Peter Lang.

Pérez Pérez, C. M. (2018). Metodología para la caracterización de patrones [verbo + objeto directo] sobre pobreza en la prensa colombiana para propósitos de análisis de sentimientos (tesis de maestría). Universidad de Antioquia, Medellín, Colombia.

Quiroz, G., Tamayo, A., & Zuluaga, J. (2017). Cuestiones metodológicas y técnicas en la recolección de un corpus de prensa con la palabra «pobreza». En Chiquito, A. & Quiroz, G. (Eds.). Pobreza, lenguaje y medios en América Latina (pp. 21-41). Berna: Peter Lang.

Quiroz, G., Chiquito, A., & Zuluaga, J. (2017). Cómo se representa lingüísticamente la pobreza en la prensa colombiana. En Chiquito, A. & Quiroz, G. (Eds.). Pobreza, lenguaje y medios en América Latina (pp. 83-107). Berna: Peter Lang.

Real Academia Española. (2017). Diccionario de la lengua española. Madrid: Espasa.

Riloff, E., & Wiebe, J. (2003). Learning Extraction Patterns for Subjective Expressions. En Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (pp. 105-112). Association for Computational Linguistics.

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based Methods for Sentiment Analysis. Computational Linguistics, 37(2), pp. 267-307.

Tamayo, A. (2018). Metodología para el análisis automático de sentimientos en documentos a partir de

técnicas de aprendizaje automático y lingüística computacional (Tesis de maestría). Universidad de Antioquia, Medellín, Colombia.

Tan, S., Wang, Y. & Cheng, X. (2008). Combining Learn-Based and Lexicon-Based Techniques for Sentiment Detection without Using Labeled Examples. SIGIR 2008.

Turney, P. D. (2002). Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. En Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (pp. 417-424). Association for Computational Linguistics.

Whitelaw, C., Garg, N., & Argamon, S. (2005). Using Appraisal Groups for Sentiment Analysis. En Proceedings of the 14th ACM International Conference on Information and Knowledge Management (pp. 625-631). ACM.