Abstract
This paper deals with the feature selection in sentiment analysis for the purpose of polarity classification. We propose a method for selecting a subset of non-redundant and discriminating features, providing better performance in classification. This method relies on the skyline paradigm often used in multi criteria decision and Database fields. To demonstrate the effectiveness of our method with regard to dimensionality reduction and classification rate, some experiments are conducted on real data sets.
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Saidani, F.R., Hadjali, A., Rassoul, I., Belkasmi, D. (2017). Skyline-Based Feature Selection for Polarity Classification in Social Networks. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_29
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DOI: https://doi.org/10.1007/978-3-319-64468-4_29
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