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Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Toc

Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis FREE CHAPTER 2. Cleaning and Validating Data 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Tokenizing text

Before we can do any real analysis of a text or a corpus of texts, we have to identify the words in the text. This process is called tokenization. The output of this process is a list of words, and possibly includes punctuation in a text. This is different from tokenizing formal languages such as programming languages: it is meant to work with natural languages and its results are less structured.

It's easy to write your own tokenizer, but there are a lot of edge and corner cases to take into consideration and account for. It's also easy to include a natural language processing (NLP) library that includes one or more tokenizers. In this recipe, we'll use the OpenNLP (http://opennlp.apache.org/) and its Clojure wrapper (https://clojars.org/clojure-opennlp).

Getting ready

We'll need to include the clojure-opennlp in our project.clj file:

(defproject com.ericrochester/text-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0&quot...
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