Affective News: The Automated Coding of Sentiment in Political Texts

Young, Lori; Soroka, Stuart

doi:10.1080/10584609.2012.671234

Cited by 405 publications

(303 citation statements)

References 54 publications

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“…The general idea of dictionaries make them relatively easy and cheap to apply across a variety of problems: identify words that separate categories and measure how often those words occur in texts (for some recent examples that use dictionaries to measure a variety of concepts, see Kellstedt 2000;Laver and Garry 2000;Burden and Sanberg 2003;Young and Soroka 2011). Finding the separating words is also relatively easy.…”

Section: Dictionary Methodsmentioning

confidence: 99%

“…This includes archives of media data (Young and Soroka 2011); floor speeches in legislatures from across the world (Quinn et al 2010); presidential, legislator, and party statements (Grimmer 2010); proposed legislation and bills (Adler and Wilkerson 2011); committee hearings (Jones, Wilkerson, and Baumgartner 2009); treaties (Spirling 2012); political science papers; and many other political texts.…”

Section: Acquiring Textmentioning

confidence: 99%

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Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Grimmer

Stewart²

2013

Polit. anal.

2,042

1,587

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Edited by R. Michael AlvarezPolitics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods-they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.

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Section: Dictionary Methodsmentioning

confidence: 99%

Section: Acquiring Textmentioning

confidence: 99%

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Grimmer

Stewart²

2013

Polit. anal.

2,042

1,587

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“…They use the Harvard Psychosociological Dictionary along with Loughran and McDonald's dictionary. Fraiberger (2016) measures valence in international news articles using dictionaries from Loughran and McDonald (2011) and Young and Soroka (2012), and uses these measures to improve GDP forecasts.…”

Section: Sentiment Text Analysismentioning

confidence: 99%

Measuring News Sentiment

Shapiro

Sudhof

Wilson

2017

ERWP

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We develop and assess new time series measures of economic sentiment based on computational text analysis of economic and financial newspaper articles from January 1980 to April 2015. The text analysis is based on predictive models estimated using machine learning techniques. We analyze four alternative news sentiment indexes.The news sentiment indexes correlate strongly with contemporaneous business cycle indicators and improve forecasting performance. A positive news sentiment shock appears consistent with an aggregate demand shock, increasing future employment, prices, and the federal funds rate. However, we find muted effects of news sentiment on future consumption. While news sentiment affects overall consumer sentiment, it has no effect on the components of consumer sentiment that drive consumption. * We thank Armen Berjikly and the Kanjoya staff for generously assisting on the project and providing guidance, comments and suggestions. Lily Huang, Kevin Vo and Catherine van der List provided excellent research assistance. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco, or the Board of Governors of the Federal Reserve System. † Federal Reserve Bank of San Francisco, adam

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“…Computational methods for political text analysis cover dictionary-based models (Kellstedt, 2000;Young and Soroka, 2012), supervised classification models (Purpura and Hillard, 2006;Stewart and Zhukov, 2009;Verberne et al, 2014;Karan et al, 2016), and unsupervised scaling models (Slapin and Proksch, 2008;Proksch and Slapin, 2010). All of these models use the discrete, word-based representations of text.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Cross-Lingual Scaling of Political Texts

Glavaš

Nanni

Ponzetto

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correlates with common political dimensions like left-to-right orientation. We propose a text scaling approach that leverages semantic representations of text and is suitable for cross-lingual political text scaling. We also propose a simple and straightforward setting for quantitative evaluation of political text scaling. Experimental results show that the semantically-informed scaling models better predict the party positions than the existing word-based models in two different political dimensions. Furthermore, the proposed models exhibit no drop in performance in the cross-lingual compared to monolingual setting.

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Affective News: The Automated Coding of Sentiment in Political Texts

Cited by 405 publications

References 54 publications

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Measuring News Sentiment

Unsupervised Cross-Lingual Scaling of Political Texts

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