文章基本信息

标题：Sentiment polarity classification of tweets using a extended dictionary
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作者：Jorge E. Camargo ; Vladimir Vargas-Calderon ; Nelson Vargas 等
期刊名称：Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence
印刷版ISSN：1137-3601
电子版ISSN：1988-3064
出版年度：2018
卷号：21
期号：62
页码：1-12
DOI：10.4114/intartif.vol21iss62pp1-12
语种：English
出版社：Spanish Association for Intelligence Artificial
摘要：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.
其他摘要：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.