Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty-aware Topic Modeling Visualization   Topic modeling is a state-of-the-art technique for analyzing text corpora. It
uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to
discover abstract topics that occur in the document collection. However, the
LDA-based topic modeling procedure is based on a randomly selected initial
configuration as well as a number of parameter values than need to be chosen.
This induces uncertainties on the topic modeling results, and visualization
methods should convey these uncertainties during the analysis process. We
propose a visual uncertainty-aware topic modeling analysis. We capture the
uncertainty by computing topic modeling ensembles and propose measures for
estimating topic modeling uncertainty from the ensemble. Then, we propose to
enhance state-of-the-art topic modeling visualization methods to convey the
uncertainty in the topic modeling process. We visualize the entire ensemble of
topic modeling results at different levels for topic and document analysis. We
apply our visualization methods to a text corpus to document the impact of
uncertainty on the analysis.

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The categories of this paper are: math
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: physics