Sentiment polarity classification of tweets using an extended dictionary

Vladimir Vargas-Calderón, Nelson A.Vargas Sánchez, Liliana Calderón-Benavides, Jorge E. Camargo

Producción científica: Artículos / NotasArtículo Científicorevisión exhaustiva

4 Citas (Scopus)

Resumen

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Española de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE’s dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE’s dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.

Idioma originalInglés
Páginas (desde-hasta)1-11
Número de páginas11
PublicaciónInteligencia Artificial
Volumen21
N.º62
DOI
EstadoPublicada - 7 sep. 2018

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