Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision |
en:manualy:kwords [2023/04/05 17:31] – [Thematic concentration] michalkren | en:manualy:kwords [2023/11/08 14:05] – jankocek |
---|
====== KWords ====== | ====== KWords ====== |
| |
{{ :manualy:k-words_logo.png?nolink&200|}} | {{ :manualy:kwords_logo_v2.png?nolink&|}} |
| |
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 ==== |
| |
Words which are highlighted in <fc #dddd00>yellow</fc> 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). |
| |
===== How it works ===== | ===== How it works ===== |
===== Application images ===== | ===== Application images ===== |
| |
[{{:kurz:kwords-vstup.png?direct&300|Inputting text into KWords}}] | {{:manualy:kwords2.png?direct&400 |}} |
[{{:kurz:kwords-vystup.png?direct&300|Analyzed text with highlighted keywords}}] | {{:manualy:kwords2_nastaveni.png?direct&400 |}} |
[{{:kurz:kwords-tab.png?direct&300|List of keywords}}] | {{:manualy:kwords2_klicova_slova.png?direct&400|}} |
[{{:kurz:kwords-distrib.png?direct&300|Distribution of keywords throughout the analyzed text}}] | {{:manualy:kwords2_graf.png?direct&400 |}} |
[{{:kurz:kwords-links.png?direct&300|Mutual relations between keywords (keyword links)}}] | {{:manualy:kwords2_distribuce.png?direct&400 |}} |
[{{:kurz:kwords-comp.png?direct&300|Comparison of several speeches -- multi-analysis}}] | {{:manualy:kwords2_konkordance.png?direct&400 |}} |
| {{:manualy:kwords2_links.png?direct&400|}} |
| |
| ===== Application images (previous version)===== |
| |
| [{{:kurz:kwords-vstup.png?direct&400 |Inputting text into KWords}}] |
| [{{:kurz:kwords-vystup.png?direct&400 |Analyzed text with highlighted keywords}}] |
| [{{:kurz:kwords-tab.png?direct&400|List of keywords}}] |
| [{{:kurz:kwords-distrib.png?direct&400 |Distribution of keywords throughout the analyzed text}}] |
| [{{:kurz:kwords-links.png?direct&400 |Mutual relations between keywords (keyword links)}}] |
| [{{:kurz:kwords-comp.png?direct&400|Comparison of several speeches -- multi-analysis}}] |
| |
==== Related links ==== | ==== Related links ==== |