Minería de opiniones basada en características guiadas por Ontologías
- Peñalver Martínez, Isidro
- García Sánchez, Francisco
- Valencia García, Rafael
ISSN: 1135-5948
Año de publicación: 2011
Número: 46
Páginas: 91-98
Tipo: Artículo
Otras publicaciones en: Procesamiento del lenguaje natural
Resumen
El éxito de la Web Social ha tenido un gran impacto en la sociedad actual y en distintas áreas de investigación. En este trabajo se propone un nuevo método para la minería de opiniones que emplea técnicas tradicionales de procesamiento de lenguaje natural junto con procesos de análisis sentimental y tecnologías de la Web Semántica. Los principales objetivos de la metodología propuesta son mejorar la minería de opiniones basada en características empleando ontologías en la selección de las mismas, así como proporcionar un nuevo método para el análisis sentimental basado en análisis vectorial.
Referencias bibliográficas
- Andreevskaia, A. y S. Bergler. 2006. “Mining wordnet for a fuzzy sentiment: Sentiment tag extraction from wordnet glosses,” Proceedings of the 11rd Conference of the European Chapter of the Association for Computational Linguistics (EACL-2006), pp. 209–216.
- Apostol, T.M. 2006. “Mathematical Analysis,” Addison-Wesley Publishing Company, Inc. Reading, Massachusetts, U.S.A.
- Balahur A. y A. Montoyo. 2009. “Semantic Approaches to Fine and Coarse-Grained Feature-Based Opinion Mining”, In Proceedings NLDB 2009, LNCS 5723, pp. 142–153 Baccianella, S., E. Andrea y F. Sebastiani. 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2200– 2204. European Language Resources Association.
- Baccianella, S., A. Esuli, y F. Sebastiani. 2009. “Multi-facet rating of product reviews,” ECIR ’09: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, pp. 461–472.
- Dave, K., S. Lawrence, y D. M. Pennock. 2003. “Mining the peanut gallery: opinion extraction and semantic classification of product reviews,” WWW ’03: Proceedings of the 12th international conference on World Wide Web, pp. 519–528.
- Esuli, A. y F. Sebastiani. 2005. “Determining the semantic orientation of terms through gloss classification,” CIKM ’05: Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 617–624.
- Esuli, A. y F. Sebastiani. 2006. ”Sentiwordnet: A publicly available lexical resource for opinion mining”. En: Proceedings of 5th Conference on Language Resources and Evaluation, LREC 2006.
- Goldberg, A. B. y X. Zhu. 2006. “Seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization,” TextGraphs ’06: Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing on the First Workshop on Graph Based Methods for Natural Language Processing, pp. 45–52.
- Hatzivassiloglous, V. y K. R. McKeown. 1997. “Predicting the semantic orientation of adjectives,” Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, pp. 174–181.
- Hu, M. y B. Liu. 2004. “Mining and summarizing customer reviews,” KDD ’04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177.
- Kamps, J. y M. Marx. 2002. “Words with attitude,” 1st International WordNet Conference, pp. 332–341.
- Kim, S. y E. Hovy. 2004. “Determining the sentiment of opinions,” COLING ’04: Proceedings of the 20th international conference on Computational Linguistics, pp. 1267–1373.
- Moreno, A., F. Pineda y R. Hidalgo. 2010. “Análisis de Valoraciones de Usuario de Hoteles con Sentitext: un sistema de análisis de sentimiento independiente del dominio”, Procesamiento del Lenguaje Natural, vol 45.
- Mukras, R. y J. Carroll. 2004. “A comparison of machine learning techniques applied to sentiment classification”.
- Pang et al. 2002. “Sentiment Classification using machine learning methods”. En EMNLP-2002.
- Pang, B. y L. Lee. 2004. “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts”', In Proceedings of the ACL, pp. 271--278.
- Shimada, K. y T. Endo. 2008. “Seeing several stars: A rating inference task for a document containing several evaluation criteria,” PAKDD 2008: Proceedings of Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, pp. 1006– 1014.
- Studer, R., V. R. Benjamins y D. Fensel. 1998. Knowledge engineering: Principles and methods, Data Knowl. Eng., vol. 25, pp. 161-197.
- Turney, P. D. y M. L. Littman. 2003. “Measuring praise and criticism: Inference of semantic orientation from association,” ACM Trans. Inf. Syst., vol. 21, no. 4, pp. 315–346.
- Zhao, L. y C. Li. 2009. “Ontology based opinion mining for movie reviews,” in Proc. of KSEM, pp. 204–214.
- Zhou, L. y P. Chaovalit. 2008. “Ontologysupported polarity mining,” Journal of the American Society for Information Science and Technology, vol. 59, no. 1, pp. 98–110.