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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
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
(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
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
(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
Research works printed but not linked: 11
- 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
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.”
Backgroundquanteda: An R package for the quantitative analysis of textual data
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
“More general textmodel functions include efficient implementations of a multinomial Naive Bayes classifier designed specifically for textual data (Manning, Raghavan, & Schütze, 2008) and latent semantic analysis (Deerwester, Dumais, Furnas, Landauer, & Harshman, 1990). quanteda also works flexibly and efficiently with…”
Good debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23062 · Cited by 513
“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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
