Analyzing Text and Social Network Data with Probabilistic Models
published: Oct. 29, 2012, recorded: September 2012, views: 5158
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Exploring and understanding large text and social network data sets is of increasing interest across multiple fields, in computer science, social science, history, medicine, and more. This talk will present an overview of recent work using probabilistic latent variable models to analyze such data. Latent variable models have a long tradition in data analysis and typically hypothesize the existence of simple unobserved phenomena to explain relatively complex observed data. In the past decade there has been substantial work on extending the scope of these approaches from relatively small simple data sets to much more complex text and network data. We will discuss the basic concepts behind these developments, reviewing key ideas, recent advances, and open issues. In addition we will highlight common ideas that lie beneath the surface of different approaches including links (for example) to work in matrix factorization. The concluding part of the talk will focus more specifically on recent work with temporal social networks, specifically data in the form of time-stamped events between nodes (such as emails exchanged among individuals over time).
Download slides: ecmlpkdd2012_smyth_probabilistic_models_01.pdf (10.0 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Write your own review or comment: