ABSTRACT

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 w

part I|2 pages

RapidMiner

chapter 1|34 pages

RapidMiner for Text Analytic Fundamentals

ByJohn Ryan

part II|2 pages

KNIME

chapter 3|18 pages

Introduction to the KNIME Text Processing Extension

ByKilian Thiel

chapter 4|12 pages

Social Media Analysis — Text Mining Meets Network Mining

ByKilian Thiel, Tobias Ko¨tter, Rosaria Silipo, and Phil Winters

part III|2 pages

Python

chapter 5|38 pages

Mining Unstructured User Reviews with Python

ByBrian Carter

chapter 6|20 pages

Sentiment Classification and Visualization of Product Review Data

ByAlexander Piazza, Pavlina Davcheva

chapter 7|20 pages

Mining Search Logs for Usage Patterns

ByTony Russell-Rose, Paul Clough

chapter 8|26 pages

Temporally Aware Online News Mining and Visualization with Python

ByPython Kyle Goslin

chapter 9|22 pages

Text Classification Using Python

ByDavid Colton

part IV|2 pages

R

chapter 10|18 pages

Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool

ByNuno Oliveira, Paulo Cortez, Nelson Areal

chapter 11|24 pages

Topic Modeling

ByPatrick Buckley

chapter 12|31 pages

Empirical Analysis of the Stack Overflow Tags Network

ByChristos Iraklis Tsatsoulis