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<Paper uid="E06-2031">
  <Title>Why Are They Excited? Identifying and Explaining Spikes in Blog Mood Levels</Title>
  <Section position="3" start_page="207" end_page="207" type="intro">
    <SectionTitle>
2 Related work
</SectionTitle>
    <Paragraph position="0"> As to burstiness phenomena in web data, Kleinberg (2002) targets email and research papers, trying to identify sharp rises in word frequencies in document streams. Bursts can be found by searching periods when a given word tends to appear at unusually short intervals. Kumar et al. (2003) extend Kleinberg's algorithm to discover dense periods of &amp;quot;bursty&amp;quot; intra-community link creation in the blogspace, while Nanno et al. (2004) extend it to work on blogs. We use a simple comparison between long-term and short-term language models associated with a given mood to identify unusual word usage patterns.</Paragraph>
    <Paragraph position="1"> Recent years have witnessed an increase in research on extracting subjective and other nonfactualaspectsoftextualcontent; see(Shanahanet al., 2005) for an overview. Much work in this area focuses on recognizing and/or annotating evaluative textual expressions. In contrast, work that explores mood annotations is relatively scarce.</Paragraph>
    <Paragraph position="2"> Mishne (2005) reports on text mining experiments aimed at automatically tagging blog posts with moods. Mishne and de Rijke (2006a) lift this work to the aggregate level, and use natural language processing and machine learning to estimate aggregate mood levels from the text of blog entries.</Paragraph>
  </Section>
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