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Learning to See: Convolutional Neural Networks for the Analysis of Social Science Data

Published online by Cambridge University Press:  16 April 2021

Michelle Torres Open the ORCID record for Michelle Torres [Opens in a new window]  and
Francisco Cantú
Michelle Torres*
Affiliation:
Rice University, Houston, TX, USA. E-mail: [email protected]
Francisco Cantú
Affiliation:
University of Houston, Houston, TX, USA. E-mail: [email protected]
*
Corresponding author Michelle Torres
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Abstract

We provide an introduction of the functioning, implementation, and challenges of convolutional neural networks (CNNs) to classify visual information in social sciences. This tool can help scholars to make more efficient the tedious task of classifying images and extracting information from them. We illustrate the implementation and impact of this methodology by coding handwritten information from vote tallies. Our paper not only demonstrates the contributions of CNNs to both scholars and policy practitioners, but also presents the practical challenges and limitations of the method, providing advice on how to deal with these issues.

Keywords

Image analysisdeep learningneural networkcomputer vision
Type
Article
Information
Political Analysis , Volume 30 , Issue 1 , January 2022 , pp. 113 - 131
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Lonna Atkeson

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https://doi.org/10.7910/DVN/UNYOLF
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