<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992020000400067</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Selección y ranking de rasgos para caracterizar textos irónicos]]></article-title>
<article-title xml:lang="en"><![CDATA[Feature Selection and Ranking to Characterize Ironic Texts]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sotolongo-Peña]]></surname>
<given-names><![CDATA[Anakarla]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arco]]></surname>
<given-names><![CDATA[Leticia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bello]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Empresa de Aplicaciones Informática Desoft  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Vrije Universiteit Brussel AI Lab, Computer Science Department ]]></institution>
<addr-line><![CDATA[ Brussels]]></addr-line>
<country>Belgium</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas Centro de Imvestigaciones Informáticas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>14</volume>
<numero>4</numero>
<fpage>67</fpage>
<lpage>84</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992020000400067&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992020000400067&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992020000400067&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Las opiniones textuales imponen grandes retos a las aplicaciones de minería de opinión ya que varios problemas están presentes; entre ellos: la escritura de opiniones de manera irónica o sarcástica. Una de las tendencias que existen para detectar la ironía consiste en la clasificación basada en rasgos. En investigación anterior se propone un conjunto de rasgos que permiten detectar ironía en opiniones textuales; sin embargo, el cálculo de estos rasgos es costoso computacionalmente. Por lo que en este artículo nos proponemos estudiar dicho conjunto de rasgos con el objetivo de detectar un subconjunto de éste que discrimine entre textos cortos irónicos y no irónicos, sin afectar la eficacia de los clasificadores. El principal resultado de este trabajo consiste en la obtención de un subconjunto de rasgos que logre detectar de manera efectiva la ironía, mediante la aplicación de técnicas de selección y de ranking de rasgos, y la evaluación de varias técnicas de aprendizaje supervisado. El conjunto obtenido de siete rasgos es suficiente para discriminar entre opiniones irónicas y no irónicas, obteniéndose resultados estadísticamente comparables con aquellos obtenidos al utilizar un conjunto mayor y más complejo de rasgos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Textual opinions impose great challenges to opinion mining applications since several problems are present; among them: writing opinions ironically or sarcastically. One of the trends that exist to detect irony is the classification based on features. In previous research a set of features that allow detecting irony in textual opinions is proposed; however, the calculation of these features is computationally costly. In this paper, we propose to study this set of features to detect a subset of it that discriminates between ironic and non-ironic short texts, without affecting the effectiveness of the classifiers. The main result of this work consists of obtaining a subset of features that can effectively detect irony, through the application of selection and feature ranking techniques, and the evaluation of several supervised learning techniques. The set obtained from seven features is enough to discriminate between ironic and non-ironic opinions, obtaining statistically comparable results with those obtained by using a larger and more complex set of features.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[detección de ironía]]></kwd>
<kwd lng="es"><![CDATA[minería de opinión]]></kwd>
<kwd lng="es"><![CDATA[selección de rasgos]]></kwd>
<kwd lng="es"><![CDATA[ranking de rasgos]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="en"><![CDATA[irony detection]]></kwd>
<kwd lng="en"><![CDATA[opinion mining]]></kwd>
<kwd lng="en"><![CDATA[feature selection]]></kwd>
<kwd lng="en"><![CDATA[ranking of features]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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