Building Dynamic Lexicons for Sentiment Analysis

• Mechulam, Nicolás ^[1] ; Salvia, Damián ^[1] ; Rosá, Aiala ; Etcheverry, Mathias ^[1]
  1. [1] Universidad de la República

     Universidad de la República

     Uruguay

     
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 22, Nº. 64, 2019 (Ejemplar dedicado a: Inteligencia Artificial (December 2019)), págs. 1-13
• Idioma: inglés
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• Resumen

  • Nowadays, many approaches for Sentiment Analysis (SA) rely on affective lexicons to identify emotions transmitted in opinions. However, most of these lexicons do not consider that a word can express different sentiments in different predication domains, introducing errors in the sentiment inference. Due to this problem, we present a model based on a context-graph which can be used for building domain specic sentiment lexicons(DL: Dynamic Lexicons) by propagating the valence of a few seed words. For different corpora, we compare the results of a simple rule-based sentiment classier using the corresponding DL, with the results obtained using a general affective lexicon. For most corpora containing specic domain opinions, the DL reaches better results than the general lexicon.