Sentiment Classification across Domains

Authors:
Dinko Lambov;Gaël Dias;Veska Noncheva
Affiliations:
Centre of Human Language Technology and Bioinformatics, University of Beira Interior, Covilhã, Portugal;Centre of Human Language Technology and Bioinformatics, University of Beira Interior, Covilhã, Portugal;Plovdiv University Paisii Hilendarski, Plovdiv, Bulgaria
Venue:
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Year:
2009

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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.