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Abstract
The polarity detection problem typically relies on experimental dictionaries, where terms are assigned polarity scores lacking contextual information. As a matter of fact, the polarity is highly dependant on the domain or community it is analysed, so we can speak of a contextual bias. We propose a method supported by fuzzy linguistic modelling to quantify this contextual bias and to enable the bias-aware sentiment analysis. To show how our approach work, we measure the bias of common concepts in two different domains and discuss the results.
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Acknowledgments
This paper has been developed with the FEDER financing under Projects TIN2013-40658-P and TIN2016-75850-R.
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Authors and Affiliations
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Juan Bernabé-Moreno, Alvaro Tejeda-Lorente & Enrique Herrera-Viedma
Department of Computer Science, University of Jaén, Jaén, Spain
Carlos Porcel
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- Alvaro Tejeda-Lorente
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Correspondence toEnrique Herrera-Viedma.
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Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Janusz Kacprzyk
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Eulalia Szmidt
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Sławomir Zadrożny
Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
Krassimir T. Atanassov
WIT - Warsaw School of Information Technology, Warsaw, Poland
Maciej Krawczak
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Bernabé-Moreno, J., Tejeda-Lorente, A., Porcel, C., Herrera-Viedma, E. (2018). A Fuzzy Linguistics Supported Model to Measure the Contextual Bias in Sentiment Polarity. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-66830-7_19
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