Unsupervised Word Polarity Tagging by Exploiting Continuous Word Representations

  1. Aitor García-Pablos
  2. Montse Cuadros
  3. German Rigau
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2015

Número: 55

Páginas: 127-134

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

El análisis de sentimiento es un campo del procesamiento del lenguaje natural que se encarga de determinar la polaridad (positiva, negativa, neutral) en los textos en los que se vierten opiniones. Un recurso habitual en los sistemas de análisis de sentimiento son los lexicones de polaridad. Un lexicón de polaridad es un diccionario que asigna un valor predeterminado de polaridad a una palabra. En este trabajo exploramos la posibilidad de generar de manera automática lexicones de polaridad adaptados a un dominio usando representaciones continuas de palabras, en concreto la popular herramienta Word2Vec. Primero mostramos una evaluación cualitativa de la polaridad sobre un pequeño conjunto de palabras, y después mostramos los resultados de nuestra competición en la tarea 12 del SemEval-2015 usando este método.

Referencias bibliográficas

  • Agerri, R. and A. Garcia. 2009. QWordNet: Extracting Polarity from WordNet Senses. Seventh Conference on International Language Resources and Evaluation Malta Retrieved May, 25:2010.
  • Baccianella, S., A. Esuli, and F. Sebastiani. 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), 0:2200–2204.
  • Bengio, Y., R. Ducharme, P. Vincent, and C. Janvin. 2003. A Neural Probabilistic Language Model. The Journal of Machine Learning Research, 3:1137–1155.
  • Brody, S. and N Elhadad. 2010. An unsupervised aspect-sentiment model for online reviews. The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, (June):804–812.
  • Choi, Y. and C. Cardie. 2009. Adapting a polarity lexicon using integer linear programming for domain-specific sentiment classification. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2- Volume 2, pages 590–598. Association for Computational Linguistics.
  • Cruz, F. L, J. A. Troyano, F. Enriquez, and J. Ortega. 2008. Clasificación de documentos basada en la opinión: experimentos con un corpus de crıticas de cine en espanol. Procesamiento de Lenguaje Natural, 41.
  • Dumais, S, G Furnas, T Landauer, S Deerwester, S Deerwester, et al. 1995. Latent semantic indexing. In Proceedings of the Text Retrieval Conference.
  • Dumais, S. T. 2004. Latent semantic analysis. Annual review of information science and technology, 38(1):188–230.
  • Esuli, A., F. Sebastiani, and V. G. Moruzzi. 2006. SentiWord-Net : A Publicly Available Lexical Resource for Opinion Mining. Proceedings of LREC 2006, pages 417–422.
  • Fellbaum, C. 1998. WordNet. Wiley Online Library.
  • Guerini, M., L. Gatti, and M. Turchi. 2013. Sentiment Analysis : How to Derive Prior Polarities from SentiWordNet. Emnlp, pages 1259–1269.
  • Hatzivassiloglou, V., V. Hatzivassiloglou, K.R. McKeown, and K.R. McKeown. 1997. Predicting the semantic orientation of adjectives. Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pages:181.
  • Huang, E. H, R. Socher, C. D. Manning, and A. Ng. 2012. Improving word representations via global context and multiple word prototypes. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pages 873–882.
  • Kim, J. 2013. Deriving adjectival scales from continuous space word representations. Emnlp, (October):1625–1630.
  • Liu, B. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1– 167.
  • Maks, I., R. Izquierdo, F. Frontini, R. Agerri, A. Azpeitia, and P. Vossen. 2014. Generating Polarity Lexicons with WordNet propagation in five languages. pages 1155–1161.
  • Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013a. Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781, pages 1–12, January.
  • Mikolov, T., I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013b. Distributed Representations of Words and Phrases and their Compositionality. arXiv preprint arXiv: . .., pages 1–9, October.
  • Mikolov, T., W. Yih, and G. Zweig. 2013. Linguistic regularities in continuous space word representations. Proceedings of NAACL-HLT, pages 746–751.
  • Pang, B. and L. Lee. 2008. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2):1–135.
  • Pavlopoulos, J. and I. Androutsopoulos. 2014 Aspect term extraction for sentiment analysis: New datasets, new evaluation measures and an improved unsupervised method. Proceedings of LASMEACL, pages 44–52. Popescu, AM and O. Etzioni. 2005. Extracting product features and opinions from reviews. Natural language processing and text mining, (October):339–346.
  • Qiu, G., B. Liu, J. Bu, and C. Chen. 2011. Opinion word expansion and target extraction through double propa-gation. Computational linguistics, (July 2010).
  • Ramos, C. and N. C. Marques. 2005. Determining the Polarity of Words through a Common Online Dictionary.
  • Socher, R, A. Perelygin, J.Y. Wu, and J. Chuang. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. newdesign.aclweb.org.
  • Taboada, M., J. Brooke, M. Tofiloski, K. Voll, and M. Stede. 2011. Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, 37(September 2010):267–307.
  • Tang, D., F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin. 2014. Learning sentimentspecific word embedding for twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1555–1565.
  • Turian, J., L. Ratinov, and Y. Bengio. 2010. Word Representations: A Simple and General Method for Semi-supervised Learning. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 384–394.
  • Turney, P. D. 2002. Thumbs Up or Thumbs Down? Semantic Orienta-tion Applied to Unsupervised Classifica-tion of Reviews. Computational Linguis-tics, (July):8.
  • Vicente, S., R. Agerri, and G. Rigau. 2014. Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages. Eacl2014.
  • Zhang, L. and B. Liu. 2014. Aspect and entity extraction for opinion mining. In Data mining and knowledge discovery for big data. Springer, pages 1–40.
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