Journal Articles

Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger in News Content

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This article examines the prevalence and nature of negativity in news content. Using dictionary-based sentiment analysis, we examine roughly fifty-five thousand front-page news stories, comparing four different affect lexicons, one for general negativity, and three capturing different measures of fear and anger. We show that fear and anger are distinct measures that capture different sentiments. It may therefore be possible to separate out fear and anger in media content, as in psychology. We also find that negativity is more strongly related to anger than to fear for each measure. This result appears to be driven by a small number of foreign policy words in the anger dictionaries, rather than an indication that negativity in U.S. coverage reflects “anger.” We highlight the importance of tailoring lexicons to domains to improve construct validity when conducting dictionary-based automation. Finally, we connect these results to existing work on the impact of emotion on political preferences and reasoning.

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Detail Information

Series Title

THE ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE

Call Number

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Publisher

Thousand Oaks : Sage Publications., 2015

Collation

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Language

English

ISBN/ISSN

978-1-5063-1463-1

Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

Volume 659, May 2015, pages 108-121

Subject(s)

Negativity
Dictionary-based sentiment analysis
Discrete emotions

Specific Detail Info

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Statement of Responsibility

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