Affective News: The Automated Coding of Sentiment in Political Texts

By Lori Young, Stuart Soroka. 2012

591 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fa26c8c77f34e2f500af22f8dbb643fb7654b585

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In Artificial Intelligence

Citations in the corpus

Text Mining in Big Data Analytics (2020, background): “Then, counting the positive and negative terms will get a score showing how much a text opposes or approves a given subject [28]:”
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013, background): “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.”
Measuring news sentiment (2020, unknown): “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 t”
quanteda: An R package for the quantitative analysis of textual data (2018, unknown): “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 a”
Framing Climate Change: Economics, Ideology, and Uncertainty in American News Media Content From 1988 to 2014 (2019, methodology): “These techniques are increasingly used by scholars in the social sciences to study news content and political discourse more broadly (Young and Soroka, 2012; Grimmer and Stewart, 2013; Lacy et al., 2015).”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, background): “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).”
Political Polarization on the Digital Sphere: A Cross-platform, Over-time Analysis of Interactional, Positional, and Affective Polarization on Social Media (2020, unknown): “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).”