This talk entitled "Exploring in the Weblog Space by Detecting Informative and Affective Articles" by researchers from Shanghai Jiao-Tong University (see full paper) describes the use of machine learning techniques to classify blogs and blog articles according to the amount of "informative" and "affective" information in the blog. Affective here is a fancy word for "touchy-feely."

The authors use various discrimination techniques and give results on which are the best for their purposes. The propose that being able to find blogs and blog articles they classify as "informative" leads to information, usually by experts, and is the kind of blog most people are interested in reading. They show how that data can be used to search for blogs that are informative, excluding affective information. Blogs could be ranked (in blog searches, for example) according to the ratio of informative to affective content.

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