Open AccessProceedings Article
Visualizing Topic Models
Allison J. B. Chaney,David M. Blei +1 more
- 20 May 2012
Vol. 6, Iss: 1, pp 419-422
TL;DR: This paper creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers, and reveals meaningful patterns in a collection, helping end-users explore and understand its contents in new ways.
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Abstract: Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our method creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers. These browsing interfaces reveal meaningful patterns in a collection, helping end-users explore and understand its contents in new ways. We provide open source software of our method.
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Citations
Structural topic models for open ended survey responses
Margaret E. Roberts,Brandon M. Stewart,Dustin Tingley,Chris Lucas,Jetson Leder-Luis,Shana Kushner Gadarian,Bethany Albertson,David G. Rand +7 more
TL;DR: The structural topic model makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects, and is illustrated with analysis of text from surveys and experiments.
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.
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling.
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Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
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.
652
References
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
•Proceedings Article
Latent Dirichlet Allocation
David M. Blei,Andrew Y. Ng,Michael I. Jordan +2 more
- 03 Jan 2001
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Probabilistic topic models
TL;DR: Surveying a suite of algorithms that offer a solution to managing large document archives suggests they are well-suited to handle large amounts of data.
5.6K
Probabilistic Topic Models
TL;DR: In this paper, a review of probabilistic topic models can be found, which can be used to summarize a large collection of documents with a smaller number of distributions over words.
•Proceedings Article
Reading Tea Leaves: How Humans Interpret Topic Models
Jonathan Chang,Sean Gerrish,Chong Wang,Jordan Boyd-Graber,David M. Blei +4 more
- 07 Dec 2009
TL;DR: New quantitative methods for measuring semantic meaning in inferred topics are presented, showing that they capture aspects of the model that are undetected by previous measures of model quality based on held-out likelihood.