Interest Analysis Using Semantic PageRank and Social Interaction Content

Social media has long been a popular resource for sentiment analysis and data mining. In this paper, we learn to predict reader interest after article reading using social interaction content in social media. The abundant interaction content (e.g., reader feedback) aims to replace typically private reader profile and browse history. Our method involves estimating interest preferences with respect to article topics and identifying quality social content concerning informativity. During interest analysis, we combine and transform articles and their reader responses into PageRank word graph to balance author- and reader-end influence. Semantic features of words, such as their content sources (authors vs. readers), syntactic parts-of-speech, and degrees of references (i.e., significances) among authors and readers, are used to weight PageRank word graph. We present the prototype system, Interest Finder, that applies the method to reader interest prediction by calculating word interestingness scores. Two sets of evaluation show that traditional, local Page Rank can more accurately cover more span of reader interest with the help of topical interest preferences learned globally, word nodes´ semantic information, and, most important of all, quality social interaction content such as reader feedback.