Abstract
Domain adaptation is usually discussed from the point of view of new algorithms that minimise performance loss when applying a classifier trained on one domain to another. However, finding pertinent data similar to the test domain is equally important for achieving high accuracy in a cross-domain task. This study proposes an algorithm for automatic estimation of performance loss in the context of cross-domain sentiment classification. We present and validate several measures of domain similarity specially designed for the sentiment classification task. We also introduce a new characteristic, called domain complexity, as another independent factor influencing performance loss, and propose various functions for its approximation. Finally, a linear regression for modeling accuracy loss is built and tested in different evaluation settings. As a result, we are able to predict the accuracy loss with an average error of 1.5% and a maximum error of 3.4%.
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Ponomareva, N., Thelwall, M. (2012). Biographies or Blenders: Which Resource Is Best for Cross-Domain Sentiment Analysis?. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_40
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DOI: https://doi.org/10.1007/978-3-642-28604-9_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28603-2
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