Recent developments in the statistical processing of textual data
作者:
Ludovic Lebart,
André Salem,
Lisette Berry,
期刊:
Applied Stochastic Models and Data Analysis
(WILEY Available online 1991)
卷期:
Volume 7,
issue 1
页码: 47-62
ISSN:8755-0024
年代: 1991
DOI:10.1002/asm.3150070106
出版商: John Wiley&Sons, Ltd.
关键词: Textual data analysis;Lexical statistics;Graphical forms and repeated segments;Open‐ended questions;Multivariate descriptive analysis
数据来源: WILEY
摘要:
AbstractStatisticians are accustomed to processing numerical, ordinal or nominal data. In many circumstances, such as socio‐economic, epidemiologic sample surveys and documentary data bases, this data is juxtaposed with textual data (for example, responses to open questions in surveys). This article presents a series of language‐independent procedures based upon applying multivariate techniques (such as correspondence analysis and clustering) to sets of generalized lexical profiles. The generalized lexical profile of a text is a vector whose components are the frequencies of each word (graphical form) or ‘repeated segment’ (sequence of words appearing with a significant frequency in the text). The processing of such large (and often sparse) vectors and matrices requires special algorithms. The main outputs are the following: (1) printouts of the characteristic words and characteristic responses for each category of respondent (these categories are generally derived from available nominal variables); (2) graphical displays of the proximities between words or segments and categories of respondents; (3) when analysing a combination of several texts: graphical displays of proximities between words or segments and each text, or between words or segments and groupings of texts. The systematic use of ‘repeated segments’ provides a valuable help in interpreting the results from a semantic po
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