Volltext-Downloads (blau) und Frontdoor-Views (grau)

Bootstrapping polarity classifiers with rule-based classification

  • 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.

Export metadata

Additional Services

Search Google Scholar


Author:Michael WiegandGND, Manfred KlennerGND, Dietrich Klakow
Parent Title (English):Language Resources and Evaluation
Place of publication:Dordrecht
Document Type:Article
Year of first Publication:2013
Date of Publication (online):2019/01/24
Tag:Bootstrapping methods; Feature engineering; Polarity classification; Sentiment analysis; Text classification
GND Keyword:Computerlinguistik; Maschinelles Lernen; Natürliche Sprache; Polarität; Text Mining
First Page:1049
Last Page:1088
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
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):License LogoUrheberrechtlich geschützt