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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

Table of Contents (24) Chapters close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Getting started with Natural Language Toolkit (NLTK)


NLTK is a powerful Python library for computational linguistics and text classification. NLTK include about 50 corpora and lexical resources such as Wordnet. NLTK is the most used tool for natural language processing in Python. It includes powerful algorithms for text tokenization, parsing, semantic reasoning, and text classification. We can find a complete guide of NLTK from http://nltk.org/.

To install NLTK, we just need to download the executable file from the website for windows and use easy_install in Linux distributions.

Tip

We may need to install PyYaml in order to use NLTK. We can download PyYaml from http://pyyaml.org/wiki/PyYAML.

NLTK defines four basic classifiers:

  • Naive Bayes

  • Maximum entropy (or Logistic regression)

  • Decision tree

  • Conditional exponential

Tip

In this chapter, we will use NLTK 3.0, which supports Python 3. However, it's still in alpha release (Sept 2013) and is likely to contain bugs. We can download the NLTK 3 from http...

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