Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It-Reference-Cited by-同舟云学术

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Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It

Published:2017 Issue: Volume: Page:
ISSN:1556-5068
Container-title:SSRN Electronic Journal
language:en
Short-container-title:SSRN Journal

Author:

Denny Matthew James,Spirling Arthur

Publisher

Elsevier BV

Reference41 articles.

1. Latent Dirichlet Allocation;David M Blei;The Journal of Machine Learning Research,2003

2. Model Selection: An Integral Part of Inference;S T Buckland;Biometrics,1997

3. Pork to Policy: The Rise of Programmatic Campaigning in Japanese Elections;Amy Catalinac;Journal of Politics,2016

4. Reading Tea Leaves: How Humans Interpret Topic Models;Jonathan Chang;Neural Information Processing Systems,2009

5. Language and Ideology in Congress;Daniel Diermeier;British Journal of Political Science,2011

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3. Collective response to the health crisis among German Twitter users: A structural topic modeling approach;International Journal of Information Management Data Insights;2022-11

4. Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection;Organizational Research Methods;2022-09-21

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