@misc{Hirst_2010_2,
  author = "Graeme Hirst and Yaroslav Riabinin and Jory Graham and Magali Boizot-Roche",
  title = "Text to ideology or text to party status?",
  year = "2010",
  note = "Unpublished presentation in the From Text to Political Positions workshop",
  month = "April",
  address = "Amsterdam",
  abstract = "A number of recent papers have used support-vector
                 machines with word features to classify political
                 texts --- in particular, legislative speech --- by
                 ideology. Our own work on this topic led us to
                 hypothesize that such classifiers are sensitive
                 not to expressions of ideology but rather to
                 expressions of attack and defence, opposition and
                 government. We test this hypothesis by training on
                 one parliament and testing on another in which party
                 roles have been interchanged, and we find that the
                 performance of the classifier completely
                 disintegrates. Moreover, some features that are
                 indicative of each party ``swap sides'' with the
                 change of government. And combining ideologically
                 inconsistent opposition parties together in the
                 classifier does not in most cases seriously degrade
                 its performance. Our results suggest that the
                 language of attack and defence, of government and
                 opposition, dominates and confounds any
                 sensitivity to ideology in these kinds of
                 classifiers.",
  download = "http://ftp.cs.toronto.edu/pub/gh/Hirst-etal-2010-T2PP.pdf"
}