Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization

Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization

Topic modeling, or identifying the set of <italic>topics</italic> that occur in a collection of articles, is one of the primary objectives of text mining. One of the big challenges in topic modeling is determining the correct number of topics: underestimating the number of topics results...

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Bibliographic Details
Main Authors: Raviteja Vangara, Manish Bhattarai, Erik Skau, Gopinath Chennupati, Hristo Djidjev, Tom Tierney, James P. Smith, Valentin G. Stanev, Boian S. Alexandrov
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Machine learning
NLP
topic modeling
semantic non-negative matrix factorization
Online Access:https://ieeexplore.ieee.org/document/9521777/

Internet

https://ieeexplore.ieee.org/document/9521777/

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