TOM: A library for topic modeling and browsing

3 years 19 days ago
TOM: A library for topic modeling and browsing
In this paper, we present TOM (TOpic Modeling), a Python library for topic modeling and browsing. Its objective is to allow for an efficient analysis of a text corpus from start to finish, via the discovery of latent topics. To this end, TOM features advanced functions for preparing and vectorizing a text corpus. It also offers a unified interface for two topic models (namely LDA using either variational inference or Gibbs sampling, and NMF using alternating leastsquare with a projected gradient method), and implements three state-of-the-art methods for estimating the optimal number of topics to model a corpus. What is more, TOM constructs an interactive Web-based browser that makes exploring a topic model and the related corpus easy.
Adrien Guille, Edmundo-Pavel Soriano-Morales
Added 03 Apr 2016
Updated 03 Apr 2016
Type Journal
Year 2016
Where FEGC
Authors Adrien Guille, Edmundo-Pavel Soriano-Morales
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