Sparse Statistical Analysis of Online News
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1 Sparse Statistical Analysis of Online News Laurent El Ghaoui (EECS/IEOR, UC Berkeley) with help from Onureena Banerjee & Brian Gawalt (EECS, UCB) BCNM Intro Talk August 27, 2008
2 Multivariate statistics in context Context: explosion in available data: heterogeneous (numerical, text, image, video) noisy (missing data, outliers, noise, etc) streaming (online learning) distributed (across networks) Results in information overload I. Challenges 1
3 New challenges Avalanche of data raises new challenges: very large-scale problems database issues (what to store and where, what to pre-compute) distributed computing (how to solve and where) online learning (how to update fast) robustness & regularization (handling noise, uncertainty) interpretability requirements, via e.g. sparsity constraints I. Challenges 2
4 Statistics for the happy few Do you remember those times when statistics involved... proprietary data, given in batch, moderate size, centralized statistical expert-assisted problem solving (model setup, feature selection, nonlinear optimization, confidence analysis, etc) solvers using nonlinear (often, unconstrained) optimization, on single machines experts from field of application to interpret the results no sparsity constraints all we cared about was statistical performance I. Challenges 3
5 A more modern point of view Large-scale, efficient statistical analysis at everyone s fingertips : automated feature selection automated confidence analysis & regularization a more analytical view of search (from the list to the short list) emphasis on interpretable results, visualization emphasis on computational complexity constraints, and (sometimes) on real-time updates I. Challenges 4
6 A few challenging applications computational biology financial markets health and medecine public social data analysis: online news, voting records, etc I. Challenges 5
7 Statistical analysis of online news New project started in 2007, with collaborators: In statistics, optimization: Bin Yu (UCB, Stat), Alexandre d Aspremont (ORFE, Princeton) In social sciences: Charles Cameron (Pol Sci, Princeton), Henry Brady (Pol Sci, UC Berkeley), Suad Joseph (Anthropology, UC Davis), Sophie Clavier (Pol Sci, SFSU) II. StatNews Project 6
8 Data Current data sets: New York Times articles, (2.5 Million articles) Reuters corpus, Reuters Significant Development corpus, Voting data from VoteWorld More to come? II. StatNews Project 7
9 Goals Understand the image (statistical associations) of a word or term as painted in the news Form a graph of words as they relate to each other Observe the evolution of the image or graph across time Understand news sources relative to each other, the propagation of concepts across news sources, and its dynamics The main challenge is to connect these soft goals with hard statistical concepts and methods II. StatNews Project 8
10 Image of a term in the news: possible approaches Given a dictionary of words or terms and a corpus of news documents Counting: raw word frequencies, tf-idf scores, co-occurence in same unit (sentence, paragraph, document, headline) Sparse regression analysis: non-zero regression coefficients correspond to relevant words Sparse covariance analysis, sparse PCA allow to build a sparse representation of words/terms (unsupervised learning) II. StatNews Project 9
11 Image dynamics visualization Sparse regressor matrix plot: Each row in the plot represents a word which, at some point in time, was statistically associated with the query word Each column to a month Columns show the classification weights assigned to the associated words by a classifier (computed over the past year, in rolling horizon fashion) Classification method: sparse logistic regression II. StatNews Project 10
12 Example: Gay in NYT headlines, chorus Sparse logistic regression analysis for gay, NYT headlines, Jan81 Jan07 20 right pride episcopal coupleadvocates r.o.t.c. priest adopt scout 100 marriage lesbian ban mainstream sailors efficiency iv marchers shepard teen II. StatNews Project 11
13 Example: Gay in NYT, Plots shows evolving image over time (from Gay Men Chorus to Right to Pride to Marriage ) Identifies sticky words (those which, once around, stay around a long time, eg, Marriage ) vs. transient ones (eg, ROTC, Virus ) Allows to highlight when shifts occur, and the overall dynamic nature of the query (fewer sticky words recently) The plot could be interactive and fun to manipulate! II. StatNews Project 12
14 Example: China neighbors in NYT headlines, II. StatNews Project 13
15 Example: Boxing neighbors in NYT headlines, II. StatNews Project 14
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