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| 008 | 181123s2016 fluaf b 001 0 eng d | |
| 010 | ▼a 2016301980 | |
| 020 | ▼a 1482237571 | |
| 020 | ▼a 9781482237573 | |
| 020 | ▼z 9781482237580 (PDF ebook) | |
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| 050 | 0 0 | ▼a QA76.9.D343 ▼b T49 2016 |
| 082 | 0 4 | ▼a 006.3/12 ▼2 23 |
| 084 | ▼a 006.312 ▼2 DDCK | |
| 090 | ▼a 006.312 ▼b T3552 | |
| 245 | 0 0 | ▼a Text mining and visualization : ▼b case studies using open-source tools / ▼c edited by Markus Hofmann, Andrew Chisholm. |
| 246 | 3 0 | ▼a Text mining, web mining, and visualization :case studies using open-source tools |
| 260 | ▼a Boca Raton : ▼b CRC Press, ▼c c2016. | |
| 300 | ▼a xl, 297 p., [10] p. of plates : ▼b ill. ; ▼c 26 cm. | |
| 490 | 1 | ▼a Chapman & Hall/CRC data mining and knowledge discovery series |
| 500 | ▼a "A Champman & Hall Book." | |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 | ▼a RapidMiner for text analytic fundamentals / John Ryan -- Empirical Zipf-Mandelbrot variation for sequential windows within documents / Andrew Chisholm -- Introduction to the KNIME text processing extention / Kilian Thiel -- Social media analysis -- text mining meets network mining / Kilian Thiel, Tobias Kötter, Rosaria Silipo, and Phil Winters -- Mining unstructured user reviews with Python / Brian Carter -- Sentiment classification and visualization of product review data / Alexander Piazza and Pavlina Davcheva -- Mining search logs for usage patterns / Tony Russell-Rose and Paul Clough -- Temporally aware online news mining and visualization with Python / Kyle Goslin -- Text classification using Python / David Colton -- Sentiment analysis of stock market behavior from Twitter using the R tool / Nun Oliverira, Paulo Cortez, and Nelson Areal -- Topic modeling / Patrick Buckley -- Empiricial analysis of the stack overflow tags network / Christos Iraklis Tsatsoulis. |
| 520 | ▼a "Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors - all highly experienced with text mining and open-source software - explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website. The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities"--Back cover. | |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Hofmann, Markus, ▼c (Computer scientist). |
| 700 | 1 | ▼a Chisholm, Andrew, ▼d 1959-, ▼e editor. |
| 830 | 0 | ▼a Chapman & Hall/CRC data mining and knowledge discovery series. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.312 T3552 | 등록번호 111799631 (3회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python.
The contributors?all highly experienced with text mining and open-source software?explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website.
The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities.
This book provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that readers can follow as part of a step-by-step, reproducible example. The examples used are available on a supplementary website.
정보제공 :
목차
RapidMiner for text analytic fundamentals / John Ryan -- Empirical Zipf-Mandelbrot variation for sequential windows within documents / Andrew Chisholm -- Introduction to the KNIME text processing extention / Kilian Thiel -- Social media analysis -- text mining meets network mining / Kilian Thiel, Tobias Kötter, Rosaria Silipo, and Phil Winters -- Mining unstructured user reviews with Python / Brian Carter -- Sentiment classification and visualization of product review data / Alexander Piazza and Pavlina Davcheva -- Mining search logs for usage patterns / Tony Russell-Rose and Paul Clough -- Temporally aware online news mining and visualization with Python / Kyle Goslin -- Text classification using Python / David Colton -- Sentiment analysis of stock market behavior from Twitter using the R tool / Nun Oliverira, Paulo Cortez, and Nelson Areal -- Topic modeling / Patrick Buckley -- Empiricial analysis of the stack overflow tags network / Christos Iraklis Tsatsoulis.