Learn about latent dirichlet allocation in Python with data from the News Articles dataset (2016) /

This dataset is designed for teaching a topic modeling technique called Latent Dirichlet Allocation (LDA), which is used to find latent topic structures in text data. The dataset is a subset of data derived from the 2016 News Articles dataset, and the example investigates the topics discussed in the...

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Bibliographic Details
Main Author:	Shi, Feng, active 2019 (Author)
Corporate Author:	Odum Institute (Author)
Format:	eBook
Language:	English
Published:	London : SAGE Publications, Ltd., 2019.
Subjects:	Computational linguistics. Python (Computer program language) Linguistique informatique. Python (Langage de programmation) computational linguistics. Computational linguistics
Online Access:	Connect to the full text of this electronic book

Description
Summary:	This dataset is designed for teaching a topic modeling technique called Latent Dirichlet Allocation (LDA), which is used to find latent topic structures in text data. The dataset is a subset of data derived from the 2016 News Articles dataset, and the example investigates the topics discussed in the news articles in an automated fashion. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Python.
Physical Description:	1 online resource : illustrations
Bibliography:	Includes bibliographical references and index.
ISBN:	9781526497727 1526497727