Sentiment polarity classification of tweets using a extended dictionary

  • Jorge E. Camargo Fundación Universitaria Konrad Lorenz
  • Vladimir Vargas-Calderon
  • Nelson Vargas
  • Liliana Calderón-Benavides Universidad Autónoma de Bucaramanga

Abstract

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.

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Published
2018-09-07
How to Cite
CAMARGO, Jorge E. et al. Sentiment polarity classification of tweets using a extended dictionary. Inteligencia Artificial, [S.l.], v. 21, n. 62, p. 1-12, sep. 2018. ISSN 1988-3064. Available at: <http://journal.iberamia.org/index.php/intartif/article/view/116>. Date accessed: 16 nov. 2018. doi: https://doi.org/10.4114/intartif.vol21iss62pp1-12.