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.
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Classifier-based Polarity Propagation in a WordNet
Semantic Scholar · Computer Science · 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.