Social and semantics analysis via non-negative matrix factorization

Social media such as Web forum often have dense interactions between user and content where network models are often appropriate for analysis. Joint non-negative matrix factorization model of participation and content data can be viewed as a bipartite graph model between users and media and is proposed for analysis social media. The factorizations allow simultaneous automatic discovery of leaders and sub-communities in the Web forum as well as the core latent topics in the forum. Results on topic detection of Web forums and cluster analysis show that social features are highly effective for forum analysis. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: Artificial Intelligence-Learning; H.3.1 [Information System]: Content Analysis and Indexing; H.5.4 [Information System]: Information interfaces and presentation--hypertext/hypermedia General Terms Theory, Algorithms Keywords Social Network Analysis, latent topic detection, latent interest detection

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Zhi-Li Wu, Chi-Wa Cheng, Chun-hung Li
• Keywords: Analysis Social Media, Internet Technology, Latent Topic Detection, Social Network Analysis, WWW 2008