The traditional notion of word meaning used in natural language processing is literal or lexical meaning as used in dictionaries and lexicons. This relatively objective notion of lexical meaning is different from more subjective notions of emotive or affective meaning. Our aim is to come to grips with subjective aspects of meaning expressed in written texts, such as the attitude or value expressed in them. This paper explores how the structure of the WordNet lexical database might be used to assess affective or emotive meaning. In particular, we construct measures based on Osgood’s semantic differential technique. Suppose we can evaluate the subjective meaning expressed in a text. This would allow us to classify documents on subjective criteria, rather than on their factual content. There are several potential applications for such classifications, for example, providing summary statistics for search engines. Given the query “Crete travel review”, a search engine could report, “There are 1000 hits of which 3/4 is a positive review”. Another potential application is filtering “flames ” for newsgroups.