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Sentiment Classification across Domains

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Progress in Artificial Intelligence (EPIA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5816))

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Abstract

In this paper we consider the problem of building models that have high sentiment classification accuracy without the aid of a labeled dataset from the target domain. For that purpose, we present and evaluate a novel method based on level of abstraction of nouns. By comparing high-level features (e.g. level of affective words, level of abstraction of nouns) and low-level features (e.g. unigrams, bigrams), we show that, high-level features are better to learn subjective language across domains. Our experimental results present accuracy levels across domains of 71.2% using SVMs learning models.

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© 2009 Springer-Verlag Berlin Heidelberg

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Lambov, D., Dias, G., Noncheva, V. (2009). Sentiment Classification across Domains. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds) Progress in Artificial Intelligence. EPIA 2009. Lecture Notes in Computer Science(), vol 5816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04686-5_51

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  • DOI: https://doi.org/10.1007/978-3-642-04686-5_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04685-8

  • Online ISBN: 978-3-642-04686-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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