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This book provides an introduction to text mining using some of the most popular and powerful opensource 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 stepbystep, reproducible example. The examples used are available on a supplementary website.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
I was really looking forward to this book, but it arrived with a few pages crinkled, and the content felt a bit scattered. It covers a lot but doesn't always connect the dots clearly. I hope the next edition improves.
L
Liam Johnson
Informative but Dense at Times
I found this book to be quite informative, especially the case studies, but some sections felt a bit dense. It’s great for those already familiar with data mining, but beginners might struggle with the jargon. Overall, a decent read.
A
Anjali Patel
Great Resource for Data Enthusiasts
This book on text mining and visualization is a fantastic resource! I love how it dives deep into open-source tools, making it really accessible for someone like me who’s just starting out. The case studies are practical and help solidify the concepts discussed.
T
Tae Lee
Packed with Useful Case Studies
As a data scientist, I appreciate the depth of the case studies included in this book. They showcase various open-source tools effectively, which is super helpful for applying theory to practice. Definitely worth a read if you’re in the field!
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⚠️ California Proposition 65 Warning: Some products sold on this website may expose you to chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm. For more information, visit www.P65Warnings.ca.gov.
