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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Lambov, D., Dias, G., & Noncheva, V. (2009). Sentiment classification across domains. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5816 LNAI, pp. 622–633). https://doi.org/10.1007/978-3-642-04686-5_51
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