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  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/275/Feature-Engineering-for-Sentiment">
    <rdfs:label><![CDATA[Feature Engineering for Sentiment]]></rdfs:label>
    <event:title><![CDATA[Feature Engineering for Sentiment]]></event:title>
    <event:speaker><person:PhDStudent rdf:about="http://ebiquity.umbc.edu/person/html/Justin/Martineau/"><person:name><![CDATA[Justin  Martineau]]></person:name><rdfs:label><![CDATA[Justin  Martineau]]></rdfs:label></person:PhDStudent></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2008-11-18T10:30:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">1999-11-30T00:00:00-05:00</event:endDate>
    <event:location><![CDATA[325b ITE]]></event:location>
    <event:abstract><![CDATA[Sentiment analysis upon free text is a difficult domain since
 free text is often informally written, poorly structured, and
 rife with spelling and grammatical errors. These
 characteristics make them difficult to parse and process with
 standard language analysis tools. These factors have made
 machine learning techniques such as bag of words support vector
 machines very popular. We describe a better feature space to
 use with support vector machines that relies upon the uneven
 distribution of words commonly seen in polar text. ]]></event:abstract>
  </event:Event>

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