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	<pub:Resource rdf:about="http://ebiquity.umbc.edu/resource/html/id/230/BlogVox2-Sentiment-Detection-in-Political-Blogs">
		<rdfs:label><![CDATA[BlogVox2: Sentiment Detection in Political Blogs]]></rdfs:label>
		<pub:type><![CDATA[Poster]]></pub:type>
		<pub:title><![CDATA[BlogVox2: Sentiment Detection in Political Blogs]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2007-07-09T00:00:00-05:00</pub:publishedOn>
		<pub:description><![CDATA[Bloggers make a huge impact on society by representing and influencing the people.
Blogging by nature is about expressing and listening to opinion. Good sentiment detection
tools, for blogs and other social media, tailored to politics can be a useful tool for
today’s society. With the elections around the corner, political blogs are vital to exerting
and keeping political influence over society. Currently, no sentiment analysis framework
that is tailored to Political Blogs exist. Hence, a modular framework built with replicable
modules for the analysis of sentiment in blogs tailored to political blogs is thus justified.
I propose Blogvox2, an information retrieval based domain independent sentiment
analysis framework that uses customized pattern matching techniques, such as nave
bayesian filter, bag of words and part of speech tagging are used for opinion extraction
in blogs. We also developed prototype two-panel and four-panel search applications of the
query results. In addition, we also analyze opinionated sentences to identify trends on the
hot and top topics.
The modular framework of of Blogvox2 provides a platform where new modules for
different domains can be easily plugged in. The framework provides the date of publishing,
permanent link and the urls of the sentences that expresses opinions based on the analysis.
Based on the analysis of the blogvox2 on political domain, our system performs well
with unigram approach. We investigated our framework with pattern matching techniques,
bigram technique, combining the unigram and bigram techiniques and incorporating parts
of speech tagging, which have not fared as well as unigram techniques. We also investigated
the reasons for the performance degradation or enhancements on each approach. Based on
our analysis, we developed different applications to ease the use of our framework.]]></pub:description>
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		<pub:counter><![CDATA[1343]]></pub:counter>
		<pub:tag><![CDATA[blog]]></pub:tag>
		<pub:tag><![CDATA[blog]]></pub:tag>
		<pub:tag><![CDATA[information retrieval]]></pub:tag>
		<pub:tag><![CDATA[opinion extraction]]></pub:tag>
		<pub:tag><![CDATA[polarity extraction]]></pub:tag>
		<pub:author>
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					<person:Person 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:Person>
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							<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Sandeep/Balijepalli"><person:name><![CDATA[Sandeep Balijepalli]]></person:name><rdfs:label><![CDATA[Sandeep Balijepalli]]></rdfs:label></person:Person>
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<person:Person 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:Person>
		</pub:firstAuthor>
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			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/resource/230.pdf]]></pub:softCopyURI>
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