Open AccessProceedings Article
On Estimation and Selection for Topic Models
Matt Taddy
- 21 Mar 2012
- pp 1184-1193
TL;DR: In this paper, the authors describe posterior maximization for topic models, identifying computational and conceptual gains from inference under a non-standard parametrization, and show that fitted parameters can be used as the basis for a novel approach to marginal likelihood estimation, via block-diagonal approximation to the information matrix, that facilitates choosing the number of latent topics.
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Abstract: This article describes posterior maximization for topic models, identifying computational and conceptual gains from inference under a non-standard parametrization. We then show that fitted parameters can be used as the basis for a novel approach to marginal likelihood estimation, via block-diagonal approximation to the information matrix, that facilitates choosing the number of latent topics. This likelihood-based model selection is complemented with a goodness-of-fit analysis built around estimated residual dispersion. Examples are provided to illustrate model selection as well as to compare our estimation against standard alternative techniques.
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Citations
stm: An R Package for Structural Topic Models
TL;DR: This paper demonstrates how to use the R package stm for structural topic modeling, which allows researchers to flexibly estimate a topic model that includes document-level metadata.
LDAvis: A method for visualizing and interpreting topics
Carson Sievert,Kenneth E. Shirley +1 more
- 01 Jan 2014
TL;DR: LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3, and a novel method for choosing which terms to present to a user to aid in the task of topic interpretation is proposed.
A model of text for experimentation in the social sciences
TL;DR: A hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates is posit, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework.
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cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data
Carmen Bravo González-Blas,Liesbeth Minnoye,Dafni Papasokrati,Sara Aibar,Gert Hulselmans,Valerie Christiaens,Kristofer Davie,Jasper Wouters,Stein Aerts +8 more
TL;DR: As an unsupervised Bayesian framework, cisTopic classifies regions in scATAC-seq data into regulatory topics, which are used for clustering and provides insight into the mechanisms underlying regulatory heterogeneity in cell populations.
CEO Behavior and Firm Performance
TL;DR: A new method to measure CEO behavior in large samples via a survey that collects high-frequency, high-dimensional diary data and a machine learning algorithm that estimates behavioral types reveals two types: “leaders,” who do multifunction,High-level meetings, and “managers,’ who do individual meetings with core functions.
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