Unsupervised Word Polarity Tagging by Exploiting Continuous Word Representations

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

ISSN: 1135-5948

Argitalpen urtea: 2015

Zenbakia: 55

Orrialdeak: 127-134

Mota: Artikulua

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Beste argitalpen batzuk: Procesamiento del lenguaje natural

Laburpena

Sentiment analysis is the area of Natural Language Processing that aims to determine the polarity (positive, negative, neutral) contained in an opinionated text. A usual resource employed in many of these approaches are the so-called polarity lexicons. A polarity lexicon acts as a dictionary that assigns a sentiment polarity value to words. In this work we explore the possibility of automatically generating domain adapted polarity lexicons employing continuous word representations, in particular the popular tool Word2Vec. First we show a qualitative evaluation of a small set of words, and then we show our results in the SemEval-2015 task 12 using the presented method.

Erreferentzia bibliografikoak

  • 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.
Datuen iturria: Dialnet