The DOI matches its Crossref record; 92 of the 94 references it cites were found in a registry or an index, and 1 are not checked yet.
Affective News: The Automated Coding of Sentiment in Political Texts
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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Cited by
Sorted by citations
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
“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.”
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Benoit, K. R., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00774 · Cited by 1,511
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“Most of the recent work has focused on the analysis of opinionated blog-writings and informal texts in social media (Young & Soroka, 2012; Mitrovic, Paltoglou, & Tadic, 2011; Bollen, Gonc¸alves, Ruan, & Mao, 2011; Bal-ahur, Kozareva, & Montoyo, 2009; Thelwall et al., 2012).”
BackgroundShapiro, A. H., Sudhof, M., & Wilson, D. J. (2020) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.07.053 · Cited by 477
“As mentioned above, we use this corpus both to construct, for a given emotion, both a lexical dictionary and a predictive model that can be used to measure the degree to which a piece of text exhibits that emotion. We first automatically construct lexicons that relate emotions to words and phrases that are indicative…”
Yarchi, M., Baden, C., & Kligler-Vilenchik, N. (2021) · Political Communication · doi:10.1080/10584609.2020.1785067 · Cited by 437
“While the text-based sentiment analysis is unable to distinguish between specific relational attitudes, it is widely established as a suitable means for gauging evaluations of a focal actor in political discourse (Young & Soroka, 2012).”
Metadata
- 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
- 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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