TY - JOUR
T1 - Transfer Learning for Topic Labeling: Analysis of the UK House of Commons Speeches 1935--2014"
AU - John, Peter
AU - Jankin, Slava
AU - Herzog, Alex
AU - Bechara, Hannah
N1 - Funding Information:
This publication was made possible (in part) by a grant from the Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author.
Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We thank University College London for financial support through its Grand Challenges.
Publisher Copyright:
© The Author(s) 2021.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3/20
Y1 - 2021/3/20
N2 - Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated.
AB - Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated.
UR - http://www.scopus.com/inward/record.url?scp=85107538903&partnerID=8YFLogxK
U2 - 10.1177/20531680211022206
DO - 10.1177/20531680211022206
M3 - Article
SN - 2053-1680
VL - 8
JO - Research and Politics
JF - Research and Politics
IS - 2
ER -