Retrieving topical sentiments from online document collections

Retrieving documents by subject matter is the general goal of information retrieval and other content access systems. There are other aspects of textual content, however, which form equally valid selection critieria. One such aspect is that of sentiment or polarity indicating the users opinion or emotional relationship with some topic. Recent work in this area has treated polarity effectively as a discrete aspect of text. In this paper we present a lightweight but robust approach to combining topic and polarity thus enabling content access systems to select content based on a certain opinion about a certain topic.