Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
Michael Wiegand and Dietrich Klakow
In: ECAI-Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), 17 Aug 2010, Lisbon, Portugal.
In this paper, we explore the effectiveness of bootstrapping
supervised machine-learning polarity classifiers using the output
of domain-independent rule-based classifiers. The benefit of this
method is that no labeled training data are required. Still, this method
allows to capture in-domain knowledge by training the supervised
classifier on in-domain features, such as bag of words.
We investigate how important the quality of the rule-based classifier
is and what features are useful for the supervised classifier. The
former addresses the issue in how far relevant constructions for polarity
classification, such as word sense disambiguation, negation modeling,
or intensification, are important for this self-training approach.
We not only compare how this method relates to conventional semi-supervised
learning but also examine how it performs under more
difficult settings in which classes are not balanced and mixed reviews
are included in the dataset.