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 <channel rdf:about="http://ebiquity.umbc.edu/tag/sentiment/">
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/371/Dynamic-Domain-Specific-Sentimental-Word-Identification-"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features">
  <title><![CDATA[Improving Binary Classification on Text Problems using Differential Word Features]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features</link>
  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference o...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis">
  <title><![CDATA[Delta TFIDF: An Improved Feature Space for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis</link>
  <description><![CDATA[Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using t...]]></description>
  <dc:date>2009-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of positive and negative attitudes from text - has received a great amount of attention over the last ten years. Over the same period, via the widespread growth in the use of what we have come to call social media, there has been an explosion in the amount of publically available user generated text on the Web. This text has the potential of providing a source of real time, time tagged sentiments from people all over the globe.

T...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis">
  <title><![CDATA[Domain Independent Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis</link>
  <description><![CDATA[Domain independent sentiment signals are words or word pairs that are present and have the same sentimental orientation in multiple domains. These words can be easily identified if you have an accurate representation of their in-domain sentimental orientation. If you also have an accurate representation of their sentimental strength then you can use them to correctly classify out of domain documents with reasonable accuracy. In this talk I will present a method to identify domain independent ...]]></description>
  <dc:date>2011-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/371/Dynamic-Domain-Specific-Sentimental-Word-Identification-">
  <title><![CDATA[Dynamic Domain Specific Sentimental Word Identification]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/371/Dynamic-Domain-Specific-Sentimental-Word-Identification-</link>
  <description><![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.
 
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 determi...]]></description>
  <dc:date>2010-11-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of
positive and negative attitudes from text - has received a great
amount of attention over the last ten years. Over the same
period, via the widespread growth in the use of what we have come
to call social media, there has been an explosion in the amount
of publically available user generated text on the Web. This text
has the potential of providing a source of real time, time tagged
sentiments from people all over the glob...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
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