Identifying subjective adjectives through web-based mutual information

This paper describes a method for ranking a large list of adjectives according to a subjectivity score without resorting to any knowledge-intensive external resources (such as lexical databases, parsers or manual annotation). The method only requires a list of adjectives to be ranked and a small set of “seeds” (manually selected subjective adjectives). The subjectivity score is obtained by computing the mutual information of pairs of adjectives taken from each set, using frequency and cooccurrence frequency counts on the World Wide Web, collected through queries to the AltaVista search engine. The obtained results improve significantly over a comparable low-resource acquisition algorithm.