@conference {17788, title = {Opinion Analysis in Document Databases}, booktitle = {Proc. AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, Stanford, CA}, year = {2006}, month = {2006///}, abstract = {There are numerous applications in which we would like toassess what opinions are being expressed in text documents. Forr 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} }