Sentiment polarity classification of tweets using a extended dictionary

• Autores: Jorge E. Camargo, Vladimir Vargas Calderon, Nelson Vargas, Liliana Calderón Benavides
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 21, Nº. 62, 2018 (Ejemplar dedicado a: Inteligencia Artificial (December 2018)), págs. 1-12
• Idioma: inglés
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• 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 Espa\~{n}ola 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.