We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Sentiment polarity classification of tweets using an extended dictionary.
- Authors
Vargas-Calderón, Vladimir; Vargas Sánchez, Nelson A.; Calderón-Benavides, Liliana; Camargo, Jorge E.
- 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 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.
- Subjects
SUPPORT vector machines; SENTIMENT analysis; POLARITY (Linguistics); SEMANTICS; COMPUTATIONAL linguistics
- Publication
Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial, 2018, Vol 21, Issue 62, p1
- ISSN
1137-3601
- Publication type
Article
- DOI
10.4114/intartif.vol21iss62pp1-11