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通过自动文本分析衡量议会辩论中的情感

Measuring Emotion in Parliamentary Debates with Automated Textual Analysis.

作者信息

Rheault Ludovic, Beelen Kaspar, Cochrane Christopher, Hirst Graeme

机构信息

Department of Political Science, University of Toronto, Toronto, Canada.

Informatics Institute, University of Amsterdam, Amsterdam, Netherlands.

出版信息

PLoS One. 2016 Dec 22;11(12):e0168843. doi: 10.1371/journal.pone.0168843. eCollection 2016.

DOI:10.1371/journal.pone.0168843
PMID:28006016
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5179059/
Abstract

An impressive breadth of interdisciplinary research suggests that emotions have an influence on human behavior. Nonetheless, we still know very little about the emotional states of those actors whose daily decisions have a lasting impact on our societies: politicians in parliament. We address this question by making use of methods of natural language processing and a digitized corpus of text data spanning a century of parliamentary debates in the United Kingdom. We use this approach to examine changes in aggregate levels of emotional polarity in the British parliament, and to test a hypothesis about the emotional response of politicians to economic recessions. Our findings suggest that, contrary to popular belief, the mood of politicians has become more positive during the past decades, and that variations in emotional polarity can be predicted by the state of the national economy.

摘要

广泛的跨学科研究表明,情绪会对人类行为产生影响。尽管如此,对于那些日常决策对我们社会有着持久影响的行为者的情绪状态,我们仍然知之甚少,这些行为者就是议会中的政治家。我们通过利用自然语言处理方法和一个数字化的文本数据语料库来解决这个问题,该语料库涵盖了英国一个世纪的议会辩论。我们采用这种方法来研究英国议会中情绪极性总体水平的变化,并检验一个关于政治家对经济衰退的情绪反应的假设。我们的研究结果表明,与普遍看法相反,在过去几十年里,政治家的情绪变得更加积极,而且情绪极性的变化可以由国民经济状况预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/3686f28ee6e7/pone.0168843.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/ab9c9bcdcee4/pone.0168843.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/931b2f957ad0/pone.0168843.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/f3fa514b7ec8/pone.0168843.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/3686f28ee6e7/pone.0168843.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/ab9c9bcdcee4/pone.0168843.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/931b2f957ad0/pone.0168843.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/f3fa514b7ec8/pone.0168843.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65d8/5179059/3686f28ee6e7/pone.0168843.g004.jpg

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