A machine learning technique used for document clustering to infer topics ( Blei, Ng & Jordan, 2003 ).
Published in Chapter:
Issues and Methods for Access, Storage, and Analysis of Data From Online Social Communities
Christopher John Quinn (Purdue University, USA), Matthew James Quinn (Williams College, USA), Alan Olinsky (Bryant University, USA), and John Thomas Quinn (Bryant University, USA)
Copyright: © 2018
|Pages: 31
DOI: 10.4018/978-1-5225-3142-5.ch015
Abstract
This chapter provides an overview for a number of important issues related to studying user interactions in an online social network. The approach of social network analysis is detailed along with important basic concepts for network models. The different ways of indicating influence within a network are provided by describing various measures such as degree centrality, betweenness centrality and closeness centrality. Network structure as represented by cliques and components with measures of connectedness defined by clustering and reciprocity are also included. With the large volume of data associated with social networks, the significance of data storage and sampling are discussed. Since verbal communication is significant within networks, textual analysis is reviewed with respect to classification techniques such as sentiment analysis and with respect to topic modeling specifically latent semantic analysis, probabilistic latent semantic analysis, latent Dirichlet allocation and alternatives. Another important area that is provided in detail is information diffusion.