| Abstract | There are numerous applications in which we would like toassess what opinions are being expressed in text documents.
Forr example, Martha Stewart’s company may have wished to
assess the degree of harshness of news articles about her in the
recent past. Likewise, a World Bank official may wish to as-
sess the degree of criticism of a proposed dam in Bangladesh.
The ability to gauge opinion on a given topic is therefore of
critical interest. In this paper, we develop a suite of algo-
rithms which take as input, a set D of documents as well as a
topic t, and gauge the degree of opinion expressed about topic
t in the set D of documents. Our algorithms can return both a
number (larger the number, more positive the opinion) as well
as a qualitative opinion (e.g. harsh, complimentary). We as-
sess the accuracy of these algorithms via human experiments
and show that the best of these algorithms can accurately re-
flect human opinions. We have also conducted performance
experiments showing that our algorithms are computationally
fast.
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