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	This ontology document is licensed under the Creative Commons
	Attribution License. To view a copy of this license, visit
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=sentiment">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=sentiment]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for sentiment]]></description>
  <items>
    <rdf:Seq>
      <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/372/Social-media-analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/335/Automatic-Domain-Adaptive-Sentiment-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/317/PhD-Proposal-Automatic-Domain-Adaptive-Sentiment-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/313/Dynamic-Domain-Adapting-Sentiment-Classifiers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/275/Feature-Engineering-for-Sentiment"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/86/Indian-Election-2009"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/56/Semantic-Discovery-Discovering-Complex-Relationships-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1033/Computational-Understanding-of-Narratives-A-Survey"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/657/TISA-Topic-Independence-Scoring-Algorithm"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis"/>
      <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/paper/html/id/429/Mining-Social-Media-Communities-and-Content"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/412/BlogVox-Learning-Sentiment-Classifiers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/366/Blogvox2-A-Modular-Domain-Independent-Sentiment-Analysis-System"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/370/On-Modeling-Trust-in-Social-Media-using-Link-Polarity"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/360/Modeling-Trust-and-Influence-in-Blogosphere-using-Link-Polarity"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/290/Automatic-Domain-Adaptive-Sentiment-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/230/BlogVox2-Sentiment-Detection-in-Political-Blogs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/231/BlogVox2-Sentiment-Detection-in-Political-Blogs"/>
      <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/resource/html/id/306/Detecting-Domain-Shift"/>
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 </channel>
 <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/372/Social-media-analytics">
  <title><![CDATA[Social media analytics]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics</link>
  <description><![CDATA[This week's ebiquity meeting will focus on social media and two research efforts that are part of our Relief Social Media project.

Mohit Kewalramani will present the topic that he is addressing in his MS research.  An important task in analyzing highly networked information sources like Twitter is to identify communities that are formed. A community can be defined as a group of nodes that have more links within the set than outside it. We plan to present a technique for detecting communiti...]]></description>
  <dc:date>2010-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</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>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/335/Automatic-Domain-Adaptive-Sentiment-Analysis">
  <title><![CDATA[Automatic Domain Adaptive Sentiment  Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/335/Automatic-Domain-Adaptive-Sentiment-Analysis</link>
  <description><![CDATA[Justin Martineau will talk about his recent work on sentiment analysis.

We'll also host the meeting online at DimDim.]]></description>
  <dc:date>2010-02-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/317/PhD-Proposal-Automatic-Domain-Adaptive-Sentiment-Analysis">
  <title><![CDATA[PhD Proposal : Automatic	Domain Adaptive Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/317/PhD-Proposal-Automatic-Domain-Adaptive-Sentiment-Analysis</link>
  <description><![CDATA[Sentiment analysis is the automatic detection and measurement of opinions and emotions expressed in text segments by machines. However, sentiment is highly domain dependent. This is particularly troubling given the scale and variety of topics seen on the web. Providing sentiment analysis on the web requires more than the standard single domain machine learning approach. In this talk I describe a plan to overcome domain dependence by breaking down documents into three different types of signal...]]></description>
  <dc:date>2009-09-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/313/Dynamic-Domain-Adapting-Sentiment-Classifiers">
  <title><![CDATA[Dynamic Domain Adapting Sentiment Classifiers]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/313/Dynamic-Domain-Adapting-Sentiment-Classifiers</link>
  <description><![CDATA[Justin Martineau will give us a
preview of his dissertation proposal.

Sentiment analysis is the automatic detection and measurement of sentiment
in text segments by machines. However, sentiment is highly domain dependent.
This is particularly troubling given the scale and variety of topics seen on
the web. Providing sentiment search on the web requires more than the
standard machine learning approach. In this talk I describe a plan to
overcome domain dependence by breaking down docum...]]></description>
  <dc:date>2009-09-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/275/Feature-Engineering-for-Sentiment">
  <title><![CDATA[Feature Engineering for Sentiment]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/275/Feature-Engineering-for-Sentiment</link>
  <description><![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
 di...]]></description>
  <dc:date>2008-11-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content">
  <title><![CDATA[Mining Social Media Communities and Content]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content</link>
  <description><![CDATA[Ph.D. Dissertation Defense


Social Media is changing the way we find information, share knowledge and
communicate with each other. The important factor contributing to the growth
of these technologies is the ability to easily produce "user-generated
content". Blogs, Twitter, Wikipedia, Flickr and YouTube are just a few
examples of Web 2.0 tools that are drastically changing the Internet landscape
today. These platforms allow users to produce, annotate and share information
with thei...]]></description>
  <dc:date>2008-10-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/86/Indian-Election-2009">
  <title><![CDATA[Indian Election 2009]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/86/Indian-Election-2009</link>
  <description><![CDATA[The project is available at http://indianelections09.umbc.edu/


This project is devoted to an analysis of the online articles, in the MSM and Social Media, about Indian Elections that will take place in 2009. We analyze the Vox Bloguli to see if it reflects Vox Populi, as far as Indian Elections 2009 are concerned. We also maintain a blog that identifies the issues, events, and personalities associated with the elections. We supplement this with an analysis of the information ecology in M...]]></description>
  <dc:date>2008-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/56/Semantic-Discovery-Discovering-Complex-Relationships-in-Semantic-Web">
  <title><![CDATA[Semantic Discovery: Discovering Complex Relationships in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/56/Semantic-Discovery-Discovering-Complex-Relationships-in-Semantic-Web</link>
  <description><![CDATA[Research in search techniques was a critical component of the first generation of the Web, and has gone from academe to mainstream. A second generation Semantic Web will be built by adding semantic annotations that software can understand and from which humans can benefit. Modeling, discovering and reasoning about complex relationships on the Semantic Web will enable this vision and transform the hunt for documents into a more automated analysis enabled by semantic technology. The beginnings ...]]></description>
  <dc:date>2003-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1033/Computational-Understanding-of-Narratives-A-Survey">
  <title><![CDATA[Computational Understanding of Narratives: A Survey]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1033/Computational-Understanding-of-Narratives-A-Survey</link>
  <description><![CDATA[Storytelling and the delivery of societal narratives enable human beings to communicate, connect, and understand one another and the world around them. Narratives can be defined as spoken, visual, or written accounts of interconnected events and actors, generally evolving through some notion of time. Today, information is typically conveyed over online communication mediums, such as social media and blogging websites. Consequently, the act of narrative delivery itself has shifted from simply ...]]></description>
  <dc:date>2022-09-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/657/TISA-Topic-Independence-Scoring-Algorithm">
  <title><![CDATA[TISA: Topic Independence Scoring Algorithm]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/657/TISA-Topic-Independence-Scoring-Algorithm</link>
  <description><![CDATA[Textual analysis using machine learning is in high demand for a wide range of applications including recommender systems, business intelligence tools, and electronic personal assistants. Some of these applications need to operate over a wide and unpredictable array of topic areas, but current in-domain, domain adaptation, and multi-domain approaches cannot adequately support this need, due to their low accuracy on topic areas that they are not trained for, slow adaptation speed, or hi...]]></description>
  <dc:date>2013-07-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis">
  <title><![CDATA[Identifying and Isolating Text Classification Signals from Domain and Genre Noise for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis</link>
  <description><![CDATA[Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This problem is generally divided into three tasks: a sentiment detection task, a topic detection task, and a sentiment measurement task. The first task attempts to determine whether the author is being objective or whether they are expressing a value judgment on the topic. The second task attempts to determine the topic of the sentiment. The third task attempts to determine whether the au...]]></description>
  <dc:date>2011-12-05</dc:date>
 </item>
 <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 TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></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/paper/html/id/429/Mining-Social-Media-Communities-and-Content">
  <title><![CDATA[Mining Social Media Communities and Content]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content</link>
  <description><![CDATA[Social Media is changing the way people find information, share
knowledge and communicate with each other. The important factor
contributing to the growth of these technologies is the ability to
easily produce “user-generated content”. Blogs, Twitter, Wikipedia,
Flickr and YouTube are just a few examples of Web 2.0 tools that are
drastically changing the Internet landscape today. These platforms
allow users to produce and annotate content and more importantly,
empower them to share...]]></description>
  <dc:date>2008-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/412/BlogVox-Learning-Sentiment-Classifiers">
  <title><![CDATA[BlogVox: Learning Sentiment Classifiers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/412/BlogVox-Learning-Sentiment-Classifiers</link>
  <description><![CDATA[Performing sentiment analysis upon a topic, specified by key words, without prior knowledge about the key words is a difficult task. With the growth of the blogosphere researchers, corporations, and politicians, among others are very interested in applying sentiment detection to blogs. To accommodate the demands from myriad users, with similarly diverse desires, a sentiment analysis engine for blogs must discover domain specific features relevant to queries in order to accurately assess the s...]]></description>
  <dc:date>2007-07-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/366/Blogvox2-A-Modular-Domain-Independent-Sentiment-Analysis-System">
  <title><![CDATA[Blogvox2: A Modular Domain Independent Sentiment Analysis System]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/366/Blogvox2-A-Modular-Domain-Independent-Sentiment-Analysis-System</link>
  <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...]]></description>
  <dc:date>2007-06-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/370/On-Modeling-Trust-in-Social-Media-using-Link-Polarity">
  <title><![CDATA[On Modeling Trust in Social Media using Link Polarity]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/370/On-Modeling-Trust-in-Social-Media-using-Link-Polarity</link>
  <description><![CDATA[There is a growing interest in exploring the role of social networks to understand how communities and individuals spread influence. In a densely connected online world, social media and networks have a great potential in influencing our thoughts and actions. We describe techniques to model trust in social media and present experimental results on finding “like minded” blogs based on blog-to-blog link sentiment for a particular domain. Using simple sentiment detection techniques, we ident...]]></description>
  <dc:date>2007-05-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/360/Modeling-Trust-and-Influence-in-Blogosphere-using-Link-Polarity">
  <title><![CDATA[Modeling Trust and Influence in Blogosphere using Link Polarity]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/360/Modeling-Trust-and-Influence-in-Blogosphere-using-Link-Polarity</link>
  <description><![CDATA[The role of social networks has been well explored in understanding how communities and individuals spread influence. In a densely connected world where much of our communication happens online, social media and networks have a great potential in influencing our thoughts and actions. We describe techniques to find "like minded" blogs based on blog-to-blog link sentiment for a particular domain. Using simple sentiment detection techniques, we identify the polarity (positive, negative or neutra...]]></description>
  <dc:date>2007-04-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/290/Automatic-Domain-Adaptive-Sentiment-Analysis">
  <title><![CDATA[Automatic Domain Adaptive Sentiment  Analysis]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/290/Automatic-Domain-Adaptive-Sentiment-Analysis</link>
  <dc:date>2010-02-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/230/BlogVox2-Sentiment-Detection-in-Political-Blogs">
  <title><![CDATA[BlogVox2: Sentiment Detection in Political Blogs]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/230/BlogVox2-Sentiment-Detection-in-Political-Blogs</link>
  <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...]]></description>
  <dc:date>2007-07-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/231/BlogVox2-Sentiment-Detection-in-Political-Blogs">
  <title><![CDATA[BlogVox2: Sentiment Detection in Political Blogs]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/231/BlogVox2-Sentiment-Detection-in-Political-Blogs</link>
  <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...]]></description>
  <dc:date>2007-07-09</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/resource/html/id/306/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild. But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy? For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream. The problem of responding to such changes when they are known to have occurred has been studied extensively. In thi...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
</rdf:RDF>
