TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Wiegand, Michael A1 - Klenner, Manfred A1 - Klakow, Dietrich T1 - Bootstrapping polarity classifiers with rule-based classification JF - Language Resources and Evaluation N2 - In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation. KW - Computerlinguistik KW - Polarität KW - Text Mining KW - Natürliche Sprache KW - Maschinelles Lernen KW - Polarity classification KW - Sentiment analysis KW - Bootstrapping methods KW - Feature engineering KW - Text classification Y1 - 2013 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84425 SN - 1574-0218 SS - 1574-0218 U6 - https://doi.org/10.1007/s10579-013-9218-3 DO - https://doi.org/10.1007/s10579-013-9218-3 N1 - This is a post-peer-review, pre-copyedit version of an article published in Language Resources and Evaluation. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10579-013-9218-3 VL - 47 IS - 4 SP - 1049 EP - 1088 PB - Springer CY - Dordrecht ER -