University of Cambridge > Talks.cam > Statistics > A Look at Partial Projections for Regression onto Text

A Look at Partial Projections for Regression onto Text

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact rbg24.

An increasingly common problem in data analysis is to infer the relationship between text and characteristics of the speaker or document source. Various modifications of the multinomial bag-of-words model are most prominent among approaches designed specifically for text regression, although many generic high-dimensional pattern recognition techniques are also applicable. We investigate one such generic technique, partial least-squares (PLS), which is commonly used in engineering and physical sciences. This inquiry is motivated by the discovery that ``slant-measure’’, a heuristic from political economics for regressing ideology onto text, is just the first PLS direction. Our goal is to provide a Bayesian analysis scheme for text regression which takes advantage of the mechanics (and initial economic motivation) of PLS , and to this end we devise model-based interpretations of the algorithm and adapt these to account for the specifics of text-count covariate matrices. Results are provided in the motivating application of ideology analysis for the 109th US Congress.

http://faculty.chicagobooth.edu/matt.taddy/

This talk is part of the Statistics series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity