Readitopics: Make Your Topic Models Readable via Labeling and Browsing

Julien Velcin; Antoine Gourru; Erwan Giry-Fouquet; Christophe Gravier; Mathieu Roche; Pascal Poncelet

Readitopics: Make Your Topic Models Readable via Labeling and Browsing

Julien Velcin ¹ Antoine Gourru ¹ Erwan Giry-Fouquet ¹ Christophe Gravier ² Mathieu Roche ³ Pascal Poncelet ⁴

1 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances

2 LHC - Laboratoire Hubert Curien [Saint Etienne]

3 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale

4 ADVANSE - ADVanced Analytics for data SciencE

LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier

Abstract : Readitopics provides a new tool for browsing a textual corpus that showcases several recent work for labeling topic models and estimating topic coherence. We will demonstrate the potential of these techniques to get a deeper understanding of the topics that structure different kinds of datasets. This tool is provided as a Web demo but it can be easily installed to experiment with your own dataset. It can be further extended to deal with more advanced topic modeling techniques.

Document type :

Conference papers

Domain :

Computer Science [cs] / Databases [cs.DB]

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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910611
Contributor : Pascal Poncelet <>
Submitted on : Thursday, November 1, 2018 - 3:18:14 PM
Last modification on : Monday, January 13, 2020 - 5:46:07 PM
Long-term archiving on: Saturday, February 2, 2019 - 1:33:21 PM

readitopics2018.pdf

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