There are numerous applications in which we would like to assess 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 assess 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 algorithms 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 assess the accuracy of these algorithms via human experiments and show that the best of these algorithms can accurately reflect human opinions. We have also conducted performance experiments showing that our algorithms are computationally fast.