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Using Word Order in Political Text Classification with Long Short-term Memory Models

Published online by Cambridge University Press:  23 December 2019

Charles Chang Open the ORCID record for Charles Chang [Opens in a new window]  and
Michael Masterson Open the ORCID record for Michael Masterson [Opens in a new window]
Charles Chang
Affiliation:
Postdoctoral Associate, The Council on East Asian Studies, Yale University, New Haven, CT06511, USA Postdoctoral Associate, Center on Religion and Chinese Society, Purdue University, West Lafayette, IN47907, USA. Email: [email protected]
Michael Masterson*
Affiliation:
PhD Candidate, Political Science at the University of Wisconsin–Madison, Madison, WI53706, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

Political scientists often wish to classify documents based on their content to measure variables, such as the ideology of political speeches or whether documents describe a Militarized Interstate Dispute. Simple classifiers often serve well in these tasks. However, if words occurring early in a document alter the meaning of words occurring later in the document, using a more complicated model that can incorporate these time-dependent relationships can increase classification accuracy. Long short-term memory (LSTM) models are a type of neural network model designed to work with data that contains time dependencies. We investigate the conditions under which these models are useful for political science text classification tasks with applications to Chinese social media posts as well as US newspaper articles. We also provide guidance for the use of LSTM models.

Keywords

statistical analysis of textsmachine learningcomputational methodsAutomated content analysis
Type
Articles
Information
Political Analysis , Volume 28 , Issue 3 , July 2020 , pp. 395 - 411
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Daniel Hopkins

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