Analyzing Text and Social Network Data with Probabilistic Models
Channel: VideoLecturesChannel
Date: 2012-11-02

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Speaker: Padhraic Smyth, Center for Machine Learning and Intelligent Systems, University of California, Irvine 

License: Creative Commons CC BY-NC-ND 3.0
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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).

0:00  Analyzing Text and Social Network Data with Probablistic Models
0:06  Acknowledgements
0:27  ECML/PKDD-2002 - 1
0:48  ECML/PKDD-2002 - 2
1:15  Facebook
2:24  Email data
3:14  Email Network
3:32  Pennsylvania Gazette
4:37  Common Themes - 1
5:12  Common Themes - 2
5:42  Modeling with Latent Variables
8:17  Statistical Latent Variable Models
9:43  Outline
10:29  Multinomial Distributions on Words
11:38  Multinomial Model for Words
12:09  Plate Diagram
12:21  Multinomial Model for Words
12:23  Plate Diagram
12:25  Multinomials = Topics
13:06  Mutliple Word Distributions
13:43  Mixture Model Clustering
14:29  Topic Model
15:15  Clusters v. Topics - 1
15:42  Clusters v. Topics - 2
15:55  Clusters v. Topics - 3
16:06  Topic Models
16:32  Topics as Matrix Factorization - 1
17:09  Topics as Matrix Factorization - 2
17:50  History
19:09  What Do We Need to Learn?
19:55  Imagine If the z´s Were Known
20:35  Learning Algorithm - 1
20:47  Learning Algorithm - 2
21:28  Learning Algorithm - 3
21:55  Example: Topics from DNA Microarray Literature
23:26  Technology v. Application Topics - 1
24:13  Technology v. Application Topics - 2
25:03  Pennsylvania Gazette Data
26:00  Enron Email Topics
27:50  "Personal" Topics ...
28:14  Political Topics
28:39  Extensions
29:27  Combining Prior Knowledge and Learned Topics
30:07  MultiLabel Document Data Sets - 1
30:55  MultiLabel Document Data Sets - 2