The computational content analyst : (Record no. 141536)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01506nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260721122818.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260721s2025 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781032846309 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 401.40285 |
| Item number | VAR-C |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vargo, Chris J. |
| 245 14 - TITLE STATEMENT | |
| Title | The computational content analyst : |
| Remainder of title | using machine learning to classify media messages / |
| Statement of responsibility, etc | Chris J. Vargo |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | New York : |
| Name of publisher, distributor, etc | Routledge, |
| Date of publication, distribution, etc | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | ix, 133 p. |
| 500 ## - GENERAL NOTE | |
| General note | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Most digital content, whether it be thousands of news articles or millions of social media posts, is too large for the naked eye alone. Often, the advent of immense datasets requires a more productive approach to labeling media beyond a team of researchers. This book offers practical guidance and Python code to traverse the vast expanses of data--significantly enhancing productivity without compromising scholarly integrity. We'll survey a wide array of computer-based classification approaches, focusing on easy-to-understand methodological explanations and best practices to ensure that your data is being labeled accurately and precisely. By reading this book, you should leave with an understanding of how to select the best computational content analysis methodology to your needs for the data and problem you have. |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Content analysis |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-07-21 | 401.40285 VAR-C | 153481 | 2026-07-21 | 2026-07-21 | Books |