This paper introduces a method for creating a subjectivity lexicon for languages with scarce resources. The method is able to build a subjectivity lexicon by using a small seed set of subjective words, an online dictionary, and a small raw corpus, coupled with a bootstrapping process that ranks new candidate words based on a similarity measure. Experiments performed with a rule-based sentence level subjectivity classifier show an 18% absolute improvement in F-measure as compared to previously proposed semi-supervised methods.
Building dictionaries for languages with scarce resources can be supported by bootstrapping approaches REF .
A Bootstrapping Method for Building Subjectivity Lexicons for Languages with Scarce Resources
LREC
Computer Science