@conference {17805,
	title = {Oasys: An opinion analysis system},
	booktitle = {AAAI Spring symposium on Computational Approaches to Analyzing Weblogs},
	year = {2004},
	month = {2004///},
	abstract = {There are numerous applications in which we would like toassess what opinions are being expressed in text documents.
For example, Martha Stewart{\textquoteright}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.
},
	author = {Cesarano,C. and Dorr, Bonnie J and Picariello, A. and Reforgiato,D. and Sagoff,A. and V.S. Subrahmanian}
}
