Martínez Cámara, EugenioCruz Mata, FermínMolina González, M. DoloresMartín Valdivia, M. TeresaOrtega Rodríguez, Francisco JavierUreña López, L. Alfonso2022-03-102022-03-102015Martínez Cámara, E., Cruz Mata, F., Molina González, M.D., Martín Valdivia, M.T., Ortega Rodríguez, F.J. y Ureña López, L.A. (2015). Improving Spanish Polarity Classification Combining Different Linguistic Resources. En NLDB 2015: 20th International Conference on Applications of Natural Language to Information Systems (234-245), Passau, Germany: Springer.978-3-319-19580-30302-9743https://hdl.handle.net/11441/130661Sentiment analysis is a challenging task which is attracting the attention of researchers. However, most of work is only focused on English documents, perhaps due to the lack of linguistic resources for other languages. In this paper, we present several Spanish opinion mining resources in order to develop a polarity classification system. In addition, we propose the combination of different features extracted from each resource in order to train a classifier over two different opinion corpora. We prove that the integration of knowledge from several resources can improve the final Spanish polarity classification system. The good results encourage us to continue developing sentiment resources for Spanish, and studying the combination of features extracted from different resourcesapplication/pdf12engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Sentiment analysisPolarity classificationLexicon-based approachSentiment feature generationinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/978-3-319-19581-0_21