<?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>0187-358X</journal-id>
<journal-title><![CDATA[Investigación bibliotecológica]]></journal-title>
<abbrev-journal-title><![CDATA[Investig. bibl]]></abbrev-journal-title>
<issn>0187-358X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Investigaciones Bibliotecológicas y de la Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-358X2022000200005</article-id>
<article-id pub-id-type="doi">10.22201/iibi.24488321xe.2022.91.58538</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Caracterización de riesgos urbanos en prensa aplicando minería de texto para el enriquecimiento de datos abiertos]]></article-title>
<article-title xml:lang="en"><![CDATA[Characterization of urban risks in the press applying text mining for the enrichment of open data]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vilches-Blázquez]]></surname>
<given-names><![CDATA[Luis M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Comesaña Ocampo]]></surname>
<given-names><![CDATA[Diana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Centro de Investigación en Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de la República de Uruguay Facultad de Información y Comunicación Departamento de Tratamiento y Transferencia de Información, Instituto de Información]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Uruguay</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>36</volume>
<numero>91</numero>
<fpage>85</fpage>
<lpage>107</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-358X2022000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-358X2022000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-358X2022000200005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las noticias se difunden libremente y con amplia disponibilidad para los usuarios de internet con mucha más facilidad que con los medios tradicionales. En estas noticias se pueden encontrar infinidad de &#8220;datos menores&#8221; ocultos que pueden suministrar valiosa información no recogida en otras fuentes de información. En este contexto, en este artículo nos ha interesado analizar y caracterizar los riesgos urbanos recogidos en prensa abierta en el contexto nacional uruguayo utilizando técnicas de minería de texto. Esta propuesta permite conformar un corpus de noticias que parte de eventos de riesgo contenidos en datos abiertos. El corpus abarca el periodo 2003-2019 y proviene de periódicos digitales abiertos (El Eco Digital, Montevideo Portal y La Red 21). Sobre este corpus se aplican diversas técnicas de minería de texto empleando el software QDA-MinerLite y el lenguaje Python (mediante la librería Scattertext) para identificar, caracterizar y descubrir conocimiento sobre estos eventos. Los resultados obtenidos del procesamiento del corpus ayudan a enriquecer los datos abiertos existentes sobre riesgos en Uruguay, incorporando información sobre sus efectos, actores e intervenciones asociadas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract News is freely spread and widely available to Internet users much more easily than traditional media. In the news, we can find an infinite number of hidden &#8220;minor data,&#8221; that can provide valuable information not collected in other sources of information. In this context, we have been interested in analyzing and characterizing the urban risks contained in the Uruguayan open newspapers using text mining techniques. This proposal makes it possible to create a news corpus based on risk events included in open data. The corpus covers 2003-2019 and is built from the digital open newspapers El Eco Digital, Montevideo Portal, and La Red 21. Various text mining techniques are applied to this corpus using the QDA-MinerLite software and the Python language (concretely, through the Scattertext library) to identify, characterize, and discover insights on these events. The corpus processing results help enrich the existing open data on risks in Uruguay, incorporating information on their effects, actors, and associated interventions.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Riesgo Urbano]]></kwd>
<kwd lng="es"><![CDATA[Minería de Texto]]></kwd>
<kwd lng="es"><![CDATA[Prensa Digital Abierta]]></kwd>
<kwd lng="es"><![CDATA[Datos Abiertos]]></kwd>
<kwd lng="en"><![CDATA[Urban Risk]]></kwd>
<kwd lng="en"><![CDATA[Text Mining]]></kwd>
<kwd lng="en"><![CDATA[Open Digital Newspapers]]></kwd>
<kwd lng="en"><![CDATA[Open Data]]></kwd>
</kwd-group>
</article-meta>
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