<?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>S2227-18992019000200105</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aprendizaje profundo para la extracción de aspectos en opiniones textuales]]></article-title>
<article-title xml:lang="en"><![CDATA[Deep learning for aspect extraction in textual opinions]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Ramos]]></surname>
<given-names><![CDATA[Dionis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arco García]]></surname>
<given-names><![CDATA[Leticia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Oriente Departamento de Informática Facultad de Ingeniería en Telecomunicaciones, Informática y Biomédica]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas. Departamento de Ciencia de la Computación Facultad de Matemática, Física y Computación]]></institution>
<addr-line><![CDATA[ Villa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Vrije Universiteit Brussel  AI Lab, Computer Science Department]]></institution>
<addr-line><![CDATA[ Brussels]]></addr-line>
<country>Belgium</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>13</volume>
<numero>2</numero>
<fpage>105</fpage>
<lpage>145</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992019000200105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992019000200105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992019000200105&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La extracción de aspectos en opiniones textuales es una tarea muy importante dentro del análisis de sentimientos o minería de opiniones, que permite lograr mayor exactitud al analizar la información y contribuir a la toma de decisiones. El aprendizaje profundo agrupa varios algoritmos o estrategias que han obtenido resultados relevantes en diversas tareas del procesamiento del lenguaje natural. Existen varios artículos de revisión sobre el análisis de sentimientos que abordan el aprendizaje profundo como una de las técnicas existentes para la extracción de aspectos; sin embargo, no existen artículos de revisión que se dediquen exclusivamente al empleo del aprendizaje profundo en el análisis de sentimiento. El objetivo de este artículo consiste en ofrecer un análisis crítico y comparativo de las principales propuestas y trabajos de revisión que emplean estrategias de aprendizaje profundo para la extracción de aspectos, profundizando en la forma de representación, modelos, resultados y conjuntos de datos empleados en esta tarea. En esta propuesta se hace el análisis de 89 artículos publicados durante el período 2011 a 2019 resaltando sus principales aciertos, fisuras, y retos de investigación. Finalmente, proponemos algunas direcciones de investigación futuras.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Aspect extraction in textual opinions is a very important task within the sentiment analysis or opinion mining, which allows achieving greater accuracy when analyzing information, and thus, contributing to decision making. Deep learning includes several algorithms or strategies that have obtained relevant results in various natural language processing tasks. There are several review papers on sentiment analysis that address deep learning as one of the existing techniques for extracting aspects; however, there are no review papers focus exclusively on to the use of deep learning in sentiment analysis. The main objective of this review paper is to offer a critical and comparative analysis of the main proposals and revision works that employ deep learning strategies for aspect extraction, by focusing on the representation approaches, principal models, obtained results and data sets used in experiments. In our proposal, the analysis of 53 papers published during the period 2011 to 2018 by highlighting their main successes, fissures, and research challenges is made. Finally, we propose some future research directions.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Minería de Opiniones]]></kwd>
<kwd lng="es"><![CDATA[Extracción de Aspectos]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje Profundo]]></kwd>
<kwd lng="es"><![CDATA[Procesamiento de Lenguaje Natural]]></kwd>
<kwd lng="en"><![CDATA[Opinion Mining]]></kwd>
<kwd lng="en"><![CDATA[Aspect Extraction]]></kwd>
<kwd lng="en"><![CDATA[Deep Learning]]></kwd>
<kwd lng="en"><![CDATA[Natural Language Processing]]></kwd>
</kwd-group>
</article-meta>
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