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Text Mining and Visualization

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analysis
Category=UNF
computational linguistics
CSV File
data mining applications
data mining methods
Data Set
document
Document Cells
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
expression
Gibbs Sampling
Input Data Table
introduction to text mining
iPython Notebook
JSON Document
JSON File
KNIME
LDA
LDA Model
LDA Topic
LDA Topic Model
machine
machine learning techniques
natural language processing
Negative Users
network methods and text data
open source analytics
preprocessing techniques
programming and visual workflow tools
Python
R
Rank Frequency Distributions
RapidMiner
regular
Search Logs
sentiment
Sentiment Analysis
Sentiment Classification
Sequential Windows
social media text analysis case studies
support
SVM
tag relationships
Tagger Node
temporal awareness
term
text classification methods
Text Processing Extension
Text Tools
Token Repository
tools
Twitter API
vector
visualization of textual data
Weka
Word Cloud

Product details

  • ISBN 9781482237573
  • Weight: 816g
  • Dimensions: 178 x 254mm
  • Publication Date: 18 Dec 2015
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description
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.
About

Markus Hofmann is a lecturer at the Institute of Technology Blanchardstown, where he focuses on the areas of data mining, text mining, data exploration and visualization, and business intelligence. Dr. Hofmann has also worked as a technology expert with 20 different organizations, such as Intel. He earned a PhD from Trinity College Dublin, an MSc in computing from the Dublin Institute of Technology, and a BA in information management systems.

Andrew Chisholm is a certified RapidMiner Master who created both basic and advanced RapidMiner video training content for RapidMinerResources.com. He has worked as a software developer, systems integrator, project manager, solution architect, customer-facing presales consultant, and strategic consultant. He earned an MSc in business intelligence and data mining from the Institute of Technology Blanchardstown and an MA in physics from Oxford University.