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Article dans une revue

Merged agreement algorithms for domain independent sentiment analysis

Abstract : In this paper, we consider the problem of building models that have high sentiment classification accuracy across domains. For that purpose, we present and evaluate three new algorithms based on multi-view learning using both high-level and low-level views, which show improved results compared to the state-of-the-art SAR algorithm [1] over cross-domain text subjectivity classification. Our experimental results present accuracy levels of 80% with two views, combining SVM classifiers over high-level features and unigrams compared to 77.1% for the SAR algorithm.
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Article dans une revue
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00659327
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : jeudi 12 janvier 2012 - 15:30:08
Dernière modification le : jeudi 24 septembre 2020 - 16:36:01

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Dinko Lambov, Sebastiao Pais, Gaël Dias. Merged agreement algorithms for domain independent sentiment analysis. Procedia - Social and Behavioral Sciences, Elsevier, 2011, 27, pp.248-257. ⟨10.1016/j.sbspro.2011.10.605⟩. ⟨hal-00659327⟩

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