Retrieving topical sentiments from online document collections

Retrieving document sby subject matter is the general goal of information retrieval and othe rcontent access systems. There are aspects of textual content, however, which form equally valid election critiria. One such aspect is that of sentiment or polarity - indicating the author's opinion or emotional relationship with some topic. Recent work in this are 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.