Abstract: The present application presents methods for performing topical sentiment analysis on electronically stored communications employing fusion of polarity and topicality. The present application also provides methods for utilizing shallow NLP techniques to determine the polarity of an expression. The present application also provides a method for tuning a domain-specific polarity lexicon for use in the polarity determination. The present application also provides methods for computing a numeric metric of the aggregate opinion about some topic expressed in a set of expressions.

Abstract: A computer assisted/implemented method for developing a classifier for classifying communications includes roughly four stages, where these stages are designed to be iterative: (1) a stage defining where and how to harvest messages (i.e., from Internet message boards, news groups and the like), which also defines an expected domain of application for the classifier; (2) a guided question/answering stage for the computerized tool to elicit the user's criteria for determining whether a message is relevant or irrelevant; (3) a labeling stage where the user examines carefully-selected messages and provides feedback about whether or not it is relevant and sometimes also what elements of the criteria were used to make the decision; and (4) a performance evaluation stage where parameters of the classifier training are optimized, the best classifier is produced, and known performance bounds are calculated.