Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian (CROSBI ID 658102)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Gombar, Paula ; Medić, Zoran ; Alagić, Domagoj ; Šnajder, Jan
engleski
Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian
Sentiment lexicons are widely used as an intuitive and inexpensive way of tackling sentiment classification, often within a simple lexicon word-counting approach or as part of a supervised model. However, it is an open question whether these approaches can compete with supervised models that use only word- representation features. We address this question in the context of domain- specific sentiment classification for Croatian. We experiment with the graph- based acquisition of sentiment lexicons, analyze their quality, and investigate how effectively they can be used in sentiment classification. Our results indicate that, even with as few as 500 labeled instances, a supervised model substantially outperforms a word-counting model. We also observe that adding lexicon-based features does not significantly improve supervised sentiment classification.
Lexical semantics ; Sentiment analysis ; Sentiment lexicons ; Croatian ; Natural language processing
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Podaci o prilogu
54-59.
2017.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
978-1-945626-45-6
Podaci o skupu
The 6th Workshop on Balto-Slavic Natural Language Processing
predavanje
04.04.2017-04.04.2017
Valencia, Španjolska