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	<event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/371/Dynamic-Domain-Specific-Sentimental-Word-Identification">
		<rdfs:label><![CDATA[Dynamic Domain Specific Sentimental Word Identification]]></rdfs:label>
		<event:title><![CDATA[Dynamic Domain Specific Sentimental Word Identification]]></event:title>
		<event:speaker>
<person:PhDAlumnus 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:PhDAlumnus>
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		<event:startDate rdf:datatype="&xsd;dateTime">2010-11-09T12:30:00-05:00</event:startDate>
		<event:endDate rdf:datatype="&xsd;dateTime">2010-11-09T14:00:00-05:00</event:endDate>
		<event:location><![CDATA[ITE 325b]]></event:location>
		<event:abstract><![CDATA[Query driven sentiment analysis is a difficult problem because the strength and polarity of sentimental word and expressions is dependent upon the topic. This necessitates a dynamic approach fast enough to operate at run time.
<p> 
In this talk I will outline the problem by presenting new experiments supporting the claim that topical sentiment is expressed by the sum of a large number of very weak signals. As a partial solution I will present a fast, statistically grounded technique to determine sentimental word orientation for a given topic. Then I will present preliminary results using document synthesis to bootstrap topical sentiment polarity classifiers at run time.
]]></event:abstract>
		<event:tag><![CDATA[genre adaptation]]></event:tag>
		<event:tag><![CDATA[information retrieval]]></event:tag>
		<event:tag><![CDATA[sentiment]]></event:tag>
		<event:host>
<person:Faculty rdf:about="http://ebiquity.umbc.edu/person/html/Shujia/Zhou"><person:name><![CDATA[Shujia Zhou]]></person:name><rdfs:label><![CDATA[Shujia Zhou]]></rdfs:label></person:Faculty>
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