A Data-Driven Analysis of Reader Viewpoints: Reconstructing the Historical Reader Using Keyword Analysis

Fidler, Masako; Cvrček, Václav

doi:10.1353/jsl.2015.0018

Cited by 20 publications

(6 citation statements)

References 24 publications

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“…For both types of analysis, we first identify those items that show a statistically significant difference (using a log-likelihood test with significance level set at 0.05), then, in the second step, these items are sorted according to an effect size metric. 4 In former studies we have used as our effect size metric the DIN (difference index), which measures the relevance of the difference simply by comparing the relative frequencies in the target texts and the reference corpus to their sum (Fidler & Cvrček, 2015;Hofland & Johansson, 1982). In order to calculate DIN, the relative frequencies are measured to put all measurements on the same scale: RelFq(Ttxt) = AbsFq(Ttxt) / N(Ttxt) 5 and RelFq(RefC) = AbsFq(RefC) / N(RefC)…”

Section: Method: Keymorph Analysismentioning

confidence: 99%

“…Keywords reveal the "Aboutness" of a text (Scott & Tribble, 2006), and keywords are connected with stylistic features (Culpeper, 2002;Baker et al, 2008;Scott, 2010). Keywords are said to show the average reader's perception of what is striking (or prominent) in a text when a target text is contrasted with a robust and well-balanced reference corpus that reflects language use of the given time (Fidler & Cvrček, 2015).…”

Section: Keymorph Analysismentioning

confidence: 99%

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The case for case in Putin’s speeches

Janda

Fidler

Cvrček³

et al. 2022

Russ Linguist

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Vladimir V. Putin has banned the use of the word ‘war’ to refer to the conflict in Ukraine. While one’s choice of words is deliberate and conscious, grammatical categories are obligatory and pivotal to signaling the roles notions have in a discourse. Over- and underrepresentation of grammatical cases can be identified by Keymorph Analysis, which measures deviations from corpus norms analogously to Keyword Analysis. This first application of Keymorph Analysis to Russian data compares the use of grammatical case for the nouns meaning ‘Russia’, ‘Ukraine’, and ‘NATO’ in Putin’s speeches in the period leading up to and immediately after the invasion of Ukraine. Our analysis reveals a narrative in which Putin depicts Russia as a dynamic, agentive, foregrounded actor, a reliable partner for collaboration, but also the victim of unfair geopolitical maneuvers. Ukraine, by contrast, is dehumanized as relatively static and backgrounded, often merely a territorial location rather than a state. NATO appears primarily as the label for an untrustworthy organization and a destination for Ukraine.

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Section: Method: Keymorph Analysismentioning

confidence: 99%

Section: Keymorph Analysismentioning

confidence: 99%

The case for case in Putin’s speeches

Janda

Fidler

Cvrček³

et al. 2022

Russ Linguist

View full text Add to dashboard Cite

show abstract

“…This relationship shows a link between words which are identified to be important to texts. In previous research, the relationship between keywords has been presented as categories or themes of keywords which express concepts in the texts or the corpus according to their research questions or some theoretical framework which is applied to their research (Culpeper, 2009;Fidler & Cvrcek, 2015;Gabrielatos & Baker, 2008;Gooberman-Hill, French, Dieppe, & Hawker, 2009;Lukac, 2015).…”

Section: Relationship Between Abstractmentioning

confidence: 99%

“…While sir and I were considered as stylistic features of the letters in common, the rest were grouped together as the words related to grammar and punctuation which is the topic discussed in the letters corpus. In another study, keywords were classified into semantically related categories which are the keywords connected to cold war, collective possession, and ideological markers according to the analytical framework for socialist discourse analysis (Fidler & Cvrcek, 2015). This thematizing method may be considered subjective because it has frequently been conducted by hand classification.…”

Section: Relationship Between Abstractmentioning

confidence: 99%

Clustering Keywords to Identify Concepts in Texts: An Analysis of Research Articles in Applied Linguistics

Pojanapunya

2018

rEFLections

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Keyword analysis is one of the most widely used methods in corpus linguistics. The method is used to generate keywords which provide an indication of concepts in texts or a corpus. Keyword analysis tools commonly produce resulting keywords presented as a list which rather poorly indicates what the corpus is about since it typically requires analysts’ knowledge on conceptual associations between keywords. Therefore, common follow-up methods of keyword analysis are to examine concordances, collocational patterns, and some other patterns of associations between keywords and contexts. This study focuses on the association within a group of keywords by constructing a representation of a keyword list as keyword clusters. The keywords for an analysis were generated from two corpora; the target corpus was collected from research articles in applied linguistics and the comparative corpus was a collection of research in pure and applied sciences. The relationship between the top 30 keywords was identifed using mutual information scores of all possible pairs of the keywords within a span of 20 and these scores were used as input for creating keyword clusters. The representations of the 30 keywords as a list and clusters are presented and discussed.

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“…The Needle-in-a-Haystack Method (NHM), elaborated by Fidler and Cvrček, provides a quantitative method for text analysis. Fidler and Cvrček (2015), for example, use corpus-linguistic methods to examine the relationship between language usage patterns and divergence in text interpretation in Czech. They analyse a set of texts (Czechoslovak presidential New Year's addresses from 1975 to 1989) and contrast the texts statistically with corpora from two different periods: one from the totalitarian period and the other from the contemporary (post-totalitarian) period.…”

Section: Bridging the Gap Between Corpus And Computational Studiesmentioning

confidence: 99%

Slavic Corpus and Computational Linguistics

Divjak¹,

Sharoff²,

Erjavec³

2017

Journal of Slavic Linguistics

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In this paper, we focus on corpus-linguistic studies that address theoretical questions and on computational linguistic work on corpus annotation, that makes corpora useful for linguistic work. First, we discuss why the corpus linguistic approach was discredited by generative linguists in the second half of the 20th century, how it made a comeback through advances in computing and was adopted by usage-based linguistics at the beginning of the 21st century. Then, we move on to an overview of necessary and common annotation layers and the issues that are encountered when performing automatic annotation, with special emphasis on Slavic languages. Finally, we survey the types of research requiring corpora that Slavic linguists are involved in world-wide, and the resources they have at their disposal.

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A Data-Driven Analysis of Reader Viewpoints: Reconstructing the Historical Reader Using Keyword Analysis

Cited by 20 publications

References 24 publications

The case for case in Putin’s speeches

The case for case in Putin’s speeches

Clustering Keywords to Identify Concepts in Texts: An Analysis of Research Articles in Applied Linguistics

Slavic Corpus and Computational Linguistics

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