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46 lines
1.8 KiB
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46 lines
1.8 KiB
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Publications
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If you use Senpy in your research, please cite `Senpy: A Pragmatic Linked Sentiment Analysis Framework <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?view=publication&task=show&id=417>`__ (`BibTex <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?controller=publications&task=export&format=bibtex&id=417>`__):
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Sánchez-Rada, J. F., Iglesias, C. A., Corcuera, I., & Araque, Ó. (2016, October).
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Senpy: A Pragmatic Linked Sentiment Analysis Framework.
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In Data Science and Advanced Analytics (DSAA),
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2016 IEEE International Conference on (pp. 735-742). IEEE.
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Senpy uses Onyx for emotion representation, first introduced in:
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Sánchez-Rada, J. F., & Iglesias, C. A. (2016).
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Onyx: A linked data approach to emotion representation.
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Information Processing & Management, 52(1), 99-114.
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Senpy uses Marl for sentiment representation, which was presented in:
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Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011).
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Linked opinions: Describing sentiments on the structured web of data.
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Senpy has been used extensively in the toolbox of the MixedEmotions project:
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Buitelaar, P., Wood, I. D., Arcan, M., McCrae, J. P., Abele, A., Robin, C., … Tummarello, G. (2018).
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MixedEmotions: An Open-Source Toolbox for Multi-Modal Emotion Analysis.
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IEEE Transactions on Multimedia.
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The representation models, formats and challenges are partially covered in a chapter of the book Sentiment Analysis in Social Networks:
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Iglesias, C. A., Sánchez-Rada, J. F., Vulcu, G., & Buitelaar, P. (2017).
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Linked Data Models for Sentiment and Emotion Analysis in Social Networks.
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In Sentiment Analysis in Social Networks (pp. 49-69).
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