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28 août 2017
- doi: 10.4000/books.aaccademia.2022
- eisbn: 9788899982553
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
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linguistica computazionale riconoscimento telefonico articolare annotazione fattualità degli eventi entità chiamata rEcognition e collegamenti nei tweet italiani etichettare per messaggi social media classificazione polarità sentimenti linguistique computationelle reconnaissance téléphonique articulatoire annotation de facturation de l'événement entité appelée rEcognition et liens dans le tweets italien étiqueter les messages des médias sociaux classement polarité sentiments computational linguistics articulatory phone recognition event factuality annotation named entity rEcognition and linking in italian tweets tagging for italian social media texts sentiment polarity classification LAN009000 Linguistics CFCiter ce document
Lucia C. Passaro et al., « Exploiting Emotive Features for the Sentiment Polarity Classification of tweets », Accademia University Press, ID : 10.4000/books.aaccademia.2022
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Résumé
This paper describes the CoLing Lab system for the participation in the constrained run of the EVALITA 2016 SENTIment POLarity Classification Task (Barbieri et al., 2016). The system extends the approach in (Passaro et al., 2014) with emotive features extracted from ItEM (Passaro et al., 2015; Passaro and Lenci, 2016) and FB-NEWS15 (Passaro et al., 2016).