Interest analysis using social interaction content with sentiments

Abstract

We introduce a method for learning to predict reader interest. In our approach, interest analysis bases on PageRank and social interaction content (e.g., reader feedback in social media). The method involves automatically estimating topical interest preferences and determining the sentiment for social content. In interest prediction, different content sources of articles and reader feedback representing readers' viewpoints are weighted accordingly and transformed into content-word weighted word graph. Then, PageRank suggests reader interest with the help of word interestingness scores. We present the prototype system, InterestFinder, that applies the method to interest analysis. Experimental evaluation shows that content source and content word weighting, and scores of interest preferences for words inferred across articles are quite helpful. Our system benefits more from subjective social interaction content than objective one in covering general readers' interest spans.