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Activity Number: 570
Type: Invited
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: JASA, Applications and Case Studies
Abstract - #306986
Title: Multinomial Inverse Regression for Text Analysis
Author(s): Matt A Taddy*+
Companies: Chicago Booth
Keywords: Inverse Regression ; Text Mining ; Nonconvex Penalization
Abstract:

Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article introduces a straightforward framework of sentiment-preserving dimension reduction for text data. Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represented as draws from a multinomial distribution, and we show how logistic regression of phrase counts onto document annotations can be used to obtain low dimension document representations that are rich in sentiment information. Guidelines for prior specification are provided, algorithm convergence is detailed, and estimator properties are outlined from the perspective of the literature on non-concave likelihood penalization. Related work on sentiment analysis from statistics, econometrics, and machine learning is surveyed and connected. Finally, the methods are applied in two detailed examples and we provide out-of-sample prediction studies to illustrate their effectiveness.


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