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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Ophir, Y., & Walter, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197520536.003.0007 · Cited by 6

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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Affective News: The Automated Coding of Sentiment in Political Texts

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