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Published in:
2009 | OriginalPaper | Chapter
Sentiment Classification across Domains
Authors : Dinko Lambov, Gaël Dias, Veska Noncheva
Published in: Progress in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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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.