| Exploring Textual Data 1998 Edition Contributor(s): Lebart, Ludovic (Author), Salem, A. (Author), Berry, L. (Author) |
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ISBN: 0792348400 ISBN-13: 9780792348405 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: December 1997 Annotation: Researchers in a number of disciplines deal with large text sets requiring both text management and text analysis. Faced with a large amount of textual data collected in marketing surveys, literary investigations, historical archives and documentary data bases, these researchers require assistance with organizing, describing and comparing texts. Exploring Textual Data demonstrates how exploratory multivariate statistical methods such as correspondence analysis and cluster analysis can be used to help investigate, assimilate and evaluate textual data. The main text does not contain any strictly mathematical demonstrations, making it accessible to a large audience. This book is very user-friendly with proofs abstracted in the appendices. Full definitions of concepts, implementations of procedures and rules for reading and interpreting results are fully explored. A succession of examples is intended to allow the reader to appreciate the variety of actual and potential applications and the complementary processing methods. A glossary of terms is provided. |
| Additional Information |
| BISAC Categories: - Language Arts & Disciplines | Library & Information Science - General |
| Dewey: 401.410 |
| LCCN: 97031927 |
| Series: Text, Speech and Language Technology |
| Physical Information: 0.77" H x 6.36" W x 9.76" (1.23 lbs) 247 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Researchers in a number of disciplines deal with large text sets requiring both text management and text analysis. Faced with a large amount of textual data collected in marketing surveys, literary investigations, historical archives and documentary data bases, these researchers require assistance with organizing, describing and comparing texts. Exploring Textual Data demonstrates how exploratory multivariate statistical methods such as correspondence analysis and cluster analysis can be used to help investigate, assimilate and evaluate textual data. The main text does not contain any strictly mathematical demonstrations, making it accessible to a large audience. This book is very user-friendly with proofs abstracted in the appendices. Full definitions of concepts, implementations of procedures and rules for reading and interpreting results are fully explored. A succession of examples is intended to allow the reader to appreciate the variety of actual and potential applications and the complementary processing methods. A glossary of terms is provided. |