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en:manualy:kwords [2021/03/09 15:39] – jankocek | en:manualy:kwords [2023/11/02 14:33] – jankocek |
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====== KWords ====== | ====== KWords ====== |
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{{ :manualy:k-words_logo.png?nolink&200|}} | {{ :manualy:k-words_logo_V2.png?nolink&200|}} |
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The KWords application is used for the analysis of texts based on their comparison with the general usage ([[en:pojmy:referencni|reference]] corpus). Its aim is to identify so-called [[en:pojmy:keyword|keywords]], which are [[en:pojmy:word|word forms]] appearing in the inspected text with a significantly higher frequency than in the reference corpus which should reflect the common usage. These key words serve as a basis for textual analysis and interpretation. | The KWords application is used for the analysis of texts based on their comparison with the general usage ([[en:pojmy:referencni|reference]] corpus). Its aim is to identify so-called [[en:pojmy:keyword|keywords]], which are [[en:pojmy:word|word forms]] appearing in the inspected text with a significantly higher frequency than in the reference corpus which should reflect the common usage. These key words serve as a basis for textual analysis and interpretation. |
==== Thematic concentration ==== | ==== Thematic concentration ==== |
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Words which are highlighted in <html><span style="background-color: yellow">yellow</span></html> in the analyzed text are those which bear thematic concentration (TC words). They are not identified through comparison with a reference corpus, but only by their placement in the frequency distribution of the units in the analyzed text: when we arrange all the words in the text from those which are most frequent and down to words which appear only once, we get a so-called [[en:pojmy:zipf|Zipf]] distribution. In this distribution we are looking for a so-called //h// point, for which we can say that rank = frequency (e.g. 32nd most frequent word has a frequency of 32 occurrences). All autosemantic words (bearing meaning independent of context) above this point (i.e. in our case with a frequency higher than 32) we label thematic concentration. More details and a specific application of this approach to literary texts can be found for example in the article of [[http://www.cechradek.cz/publ/2013_Davidova_Cech_Tematicka_koncentrace_Jehlicka_NR.pdf|R. Čech]] (2013). | Words which are highlighted in yellow in the analyzed text are those which bear thematic concentration (TC words). They are not identified through comparison with a reference corpus, but only by their placement in the frequency distribution of the units in the analyzed text: when we arrange all the words in the text from those which are most frequent and down to words which appear only once, we get a so-called [[en:pojmy:zipf|Zipf]] distribution. In this distribution we are looking for a so-called //h// point, for which we can say that rank = frequency (e.g. 32nd most frequent word has a frequency of 32 occurrences). All autosemantic words (bearing meaning independent of context) above this point (i.e. in our case with a frequency higher than 32) we label thematic concentration. More details and a specific application of this approach to literary texts can be found for example in the article of [[http://www.cechradek.cz/publ/2013_Davidova_Cech_Tematicka_koncentrace_Jehlicka_NR.pdf|R. Čech]] (2013). |
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===== How it works ===== | ===== How it works ===== |