Classifier-based Polarity Propagation in a WordNet
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Abstract
In this paper we present a novel approach to the construction of an extensive, sense-level sentiment lexicon built on the basis of a wordnet. The main aim of this work is to create a high-quality sentiment lexicon in a partially automated way. We propose a method called Classifier-based Polarity Propagation, which utilises a very rich set of wordnet-based features, to recognize and assign specific sentiment polarity values to wordnet senses. We have demonstrated that in comparison to the existing rule-base solutions using specific, narrow set of semantic relations, our method allows for the construction of a more reliable sentiment lexicon, starting with the same seed of annotated synsets.