Text as data : a new framework for machine learning and the social sciences /
"From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanw...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | , |
| Formato: | Libro |
| Lenguaje: | Inglés |
| Publicado: |
Princeton :
Princeton University Press,
c2022.
|
| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 03621cam a2200385 a 4500 | ||
|---|---|---|---|
| 001 | 99919132204151 | ||
| 005 | 20230320143904.0 | ||
| 008 | 220110s2022 njua b 001 0 eng d | ||
| 020 | |a 9780691207544 |q (hardcover) | ||
| 020 | |a 0691207542 |q (hardcover) | ||
| 020 | |a 9780691207551 |q (paperback) | ||
| 020 | |a 0691207550 |q (paperback) | ||
| 020 | |a 9780691207995 |q (ebook) | ||
| 020 | |a 0691207992 |q (ebook) | ||
| 035 | |a (OCoLC)1295105650 | ||
| 035 | |a (OCoLC)on1295105650 | ||
| 040 | |a UKMGB |c UKMGB |d OCLCO |d U@S | ||
| 049 | |a U@SA | ||
| 050 | 1 | 4 | |a QA76.9.D343 |b G75 2022 |
| 082 | 0 | 4 | |a 006.3/12 |2 23 |
| 100 | 1 | |a Grimmer, Justin. | |
| 245 | 1 | 0 | |a Text as data : |b a new framework for machine learning and the social sciences / |c Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart. |
| 260 | |a Princeton : |b Princeton University Press, |c c2022. | ||
| 300 | |a xix, 336 p. : |b il. ; |c 26 cm. | ||
| 504 | |a Incluye referencias bibliográficas (p. [307]-329) e índice.50 | ||
| 505 | 0 | |a Part I. Preliminarie: 1. Introduction ; 2. Social science research and text analysis -- Part II. Selection and representation: 3. Principles of selection and representation ; 4. Selecting documents ; 5. Bag of words ; 6. The multinominal language model ; 7. The vector space model and similarity metrics ; 8. Distributed representations of words ; 9. Representations from language sequences -- Part III. Discovery: 10. Principles of discovery ; 11. Discriminating words ; 12. Clustering ; 13. Topic models ; 14. Low-dimensional document embeddings -- Part IV. Measurement: 15. Principles of measurement ; 16. Word counting ; 17. An overview of supervised classification ; 18. Coding a training set ; 19. Classifying documents with supervised learning ; 20. Checking performance -- 21. Repurposing discovery methods -- Part V. Inference: 22. Principles of inference ; 23. Prediction ; 24. Casual inference ; 25. Text as outcome ; 26. Text as treatment ; 27. Text as confounder -- Part VI. Conclusion. | |
| 520 | |a "From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.Text as Data is organized around the core tasks in research projects using text--representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides--computer science and social science, the qualitative and the quantitative, and industry and academia--Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain." --Descripción del editor. | ||
| 650 | 0 | |a Text data mining. | |
| 650 | 0 | |a Social sciences |x Data processing. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 7 | |a Minería de datos de texto. |2 UDESA | |
| 650 | 7 | |a Ciencias sociales |x Procesamiento de datos. |2 UDESA | |
| 650 | 7 | |a Aprendizaje automático. |2 UDESA | |
| 700 | 1 | |a Roberts, Margaret E. | |
| 700 | 1 | |a Stewart, Brandon M. | |