Cited by 610Sentiment Analysis and Opinion MiningComputational and Text Analysis MethodsNews Media Framing and Political Communication

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Affective News: The Automated Coding of Sentiment in Political Texts

Young, Lori, Soroka, Stuart·2012·Political Communication

DOI 10.1080/10584609.2012.671234

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Abstract

An increasing number of studies in political communication focus on the “sentiment” or “tone” of news content, political speeches, or advertisements. This growing interest in measuring sentiment coincides with a dramatic increase in the volume of digitized information. Computer automation has a great deal of potential in this new media environment. The objective here is to outline and validate a new automated measurement instrument for sentiment analysis in political texts. Our instrument uses a dictionary-based approach consisting of a simple word count of the frequency of keywords in a text from a predefined dictionary. The design of the freely available Lexicoder Sentiment Dictionary (LSD) is discussed in detail here. The dictionary is tested against a body of human-coded news content, and the resulting codes are also compared to results from nine existing content-analytic dictionaries. Analyses suggest that the LSD produces results that are more systematically related to human coding than are results based on the other available dictionaries. The LSD is thus a useful starting point for a revived discussion about dictionary construction and validation in sentiment analysis for political communication.

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Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
Untitled work
(1994) · DigitalGeorgetown (Georgetown University Library) · Cited by 10,542
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,271
DESCARTES' ERROR: Emotion, Reason, and the Human Brain
Marg, E. (1995) · Optometry and Vision Science · doi:10.1002/j.1538-9235.1995.tb03988.x · Cited by 8,178
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
Untitled work
(1996) · Cited by 3,692
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · doi:10.3115/1220575.1220619 · Cited by 3,388
Psychological Aspects of Natural Language Use: Our Words, Our Selves
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145041 · Cited by 2,624

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Bradley M. M., 1999, Affective Norms for English Words (ANEW): Stimuli, instruction manual and affective ratings
Grefenstette G., 2004, Exploring attitude and affect in text: Theories and applications, 93
Hart R. P., 2000, DICTION 5.0: The text analysis program
Huitt W., 2003, The affective system: Educational psychology interactive
Krueger J. S., 2005, The place of prediction in politics
Mishne G., 2005, Experiments with mood classification in blog posts
Murphy C., 2006, The rhetorical semantics of state ballot initiative arguments in California, 1980–2004
Nussbaum M., 2004, Hiding from humanity: Shame, disgust and the law
Roget P. M., 1911, Roget's thesaurus of English words and phrases (supplemented electronic version)
Tong R., 2001, Detecting and tracking opinions in online discussions
Whissell C., 1989, Emotion: Theory and research, 113

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Authors: Young, L., & Soroka, S.
Affiliations: University of Pennsylvania (US)
McGill University (CA)
Corresponding author: Soroka, Stuart
ORCID: Soroka, Stuart
Venue: Political Communication
Publisher: Informa UK Limited
Published: 2012-04
Volume: 29
Issue: 2
Pages: 205-231
ISSN: 1058-4609 (ISSN-L)
1091-7675
DOI: 10.1080/10584609.2012.671234
OpenAlex: W2072514260
Semantic Scholar: fa26c8c77f34e2f500af22f8dbb643fb7654b585
Cited by: 609
References: 94
Citations by year: 2026: 35 · 2025: 46 · 2024: 55 · 2023: 60 · 2022: 53 · 2021: 82 · 2020: 68 · 2019: 56 · 2018: 42 · 2017: 32 · 2016: 19 · 2015: 20 · 2014: 12 · 2013: 26 · 2012: 2
FWCI: 19.21
Type: journal article
Language: en
Open access: closed
Locations: Political Communication (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Social Sciences › Communication › News Media Framing and Political Communication
Keywords: sentiment analysis, lexicon-based sentiment analysis, political news, computational text analysis

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Affective News: The Automated Coding of Sentiment in Political Texts

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