Discovering User Interest in Social Media Based on Correlation

Recently, with the express growth of social media, users have joined more and more of these networks and live their lives virtually. Consequently, they create a huge amount of data on these social media sites, and they become data resources for information processing and have been widely investigated in computer science. Discovering users interests on social media is a problem that has received a lot of attention because it has high applicability in practice. The purpose of this paper is to introduce a method to detect user-interest topics on social media by analyzing the content of user’ posts. Research used a semantic expansion technique based on the Wikipedia dictionary and the N-gram technique to split; it used the TF.IDF weighted vector to represent and estimate based on Pearson correlation. The experimental results show that the research model can be applied to the analysis of many social media sites with many different languages, regardless of the network structure and language used on these social media.