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Article

Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph

1
Department of Mathematics, University of Rijeka, R. Matejčić 2, 51000 Rijeka, Croatia
2
Faculty of Humanities and Social Sciences, University of Rijeka, Sveučilišna Avenija 4, 51000 Rijeka, Croatia
*
Author to whom correspondence should be addressed.
Academic Editors: Jonatan Lerga, Ljubisa Stankovic, Nicoletta Saulig and Cornel Ioana
Mathematics 2021, 9(12), 1449; https://doi.org/10.3390/math9121449
Received: 20 April 2021 / Revised: 6 June 2021 / Accepted: 12 June 2021 / Published: 21 June 2021
(This article belongs to the Special Issue New Trends in Graph and Complexity Based Data Analysis and Processing)
This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical dependency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous lexemes with respect to a specific corpus. The proposed approach has the potential to be used as a complementary method to other NLP resources and tasks, including word disambiguation, domain relatedness, sense structure, metaphoricity, as well as a cross- and intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations. View Full-Text
Keywords: lexical graph analysis; corpus; knowledge representation and reasoning; affective computing; sentiment analysis lexical graph analysis; corpus; knowledge representation and reasoning; affective computing; sentiment analysis
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MDPI and ACS Style

Ban Kirigin, T.; Bujačić Babić, S.; Perak, B. Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph. Mathematics 2021, 9, 1449. https://doi.org/10.3390/math9121449

AMA Style

Ban Kirigin T, Bujačić Babić S, Perak B. Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph. Mathematics. 2021; 9(12):1449. https://doi.org/10.3390/math9121449

Chicago/Turabian Style

Ban Kirigin, Tajana, Sanda Bujačić Babić, and Benedikt Perak. 2021. "Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph" Mathematics 9, no. 12: 1449. https://doi.org/10.3390/math9121449

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