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.
@InProceedings{KOCOŃ18.1000, author = {Jan Kocoń and Arkadiusz Janz and Maciej Piasecki}, title = "{Classifier-based Polarity Propagation in a WordNet}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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