Learning a statistical model of product aspects for sentiment analysis

• Autores: Lisette García Moya, Rafael Berlanga , Henry Anaya Sánchez
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 49, 2012, págs. 157-162
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen
  • español

    En este artculo se introduce una nueva metodologa para modelar ca- ractersticas de productos a partir de una coleccion de opiniones de usuarios. La metodologa propuesta se basa en modelos estadsticos de lenguajes y es aplicable a productos de dominio arbitrario. La metodologa combina un kernel de palabras de opinion con un modelo de traduccion de palabras para estimar el modelo de caractersticas. Se presenta ademas un metodo para modelar las opiniones vertidas sobre las caractersticas. Los experimentos realizados sobre diferentes colecciones de opiniones muestran resultados alentadores en el modelado tanto de caractersticas como de opiniones vertidas sobre estas.

  • English

    In this paper, we introduce a new methodology for modeling product aspects from a collection of free-text customer reviews. The proposal relies on a lan- guage modeling framework and is domain independent. It combines both a kernel- based model of opinion words and a stochastic translation model between words to approach the aspect model of products. We also present a ranking-based met- hodology to model the sentiments expressed about the aspects. The experiments carried out over several collections of customer reviews show encouraging results in the modeling of product aspects and their sentiments even from individual customer reviews.

• Referencias bibliográficas
  • Carenini, G., R. Ng, and A. Pauls. 2006. Multi-document summarization of evaluative text. In Proc. of EACL 2006, pages 305-312.
  • Cruz, F.L., J.A. Troyano, F. Enríquez, F.J. Ortega, and C.G. Vallejo. 2010. A knowledge-rich approach to feature-based opinion extraction...
  • Davies, Mark. 2011. Word frequency data from the Corpus of Contemporary American English (COCA). Downloaded from http://www.wordfrequency.info...
  • De Marneffe, M.C., B. MacCartney, and C.D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings...
  • Dillon, J., Y. Mao, G. Lebanon, and J. Zhang. 2007. Statistical Translation, Heat Kernels, and Expected Distance. In Proc. of the 23rd Conference...
  • Ding, Xiaowen, Bing Liu, and Philip S. Yu. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the international...
  • García-Moya, Lisette, Henry Anaya-Sánchez, and Rafael Berlanga-Llavori. 2012. Combining Probabilistic Language Models for Aspect-Based Sentiment...
  • Hu, M and B Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge...
  • Qiu, Guang, Bing Liu, Jiajun Bu, and Chun Chen. 2009. Expanding domain sentiment lexicon through double propagation. In Proceedings of the...
  • Wu, Yuanbin, Qi Zhang, Xuanjing Huang, and Lide Wu. 2009. Phrase dependency parsing for opinion mining. In Proceedings of the 2009 Conference...
  • Yu, J., Z.J. Zha, M. Wang, and T.S. Chua. 2011. Aspect ranking: identifying important product aspects from online consumer reviews. In Proc....