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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

Word cloud visualization of the most common positive words in tweets


In this example, we will develop a simple application that counts the number of occurrences of each word in the positive tweets. First, we will split each tweet into words. Then, we remove all the URLs (http://...) and twitter users (@...). Next, we will remove all the words with three or less characters (such as the, why, she, him, and so on). Finally, the counted word frequencies will be visualized into a word cloud. In the code listed as follows, we implement the JavaScript map function to split words from tweets:

function(){
  this.text.split(' ').forEach(
      function(word){
        var txt = word.toLowerCase();
          if(!(/^@/).test(txt) &&
            txt.length >= 3 &&
          !(/^http/).test(txt)){
       emit(txt,1)
      }
    }
}

The input will look similar to the following code snippet:

'text': '@SomeUsr After using LaTeX a lot any other typeset mathematics just looks greate. http:...
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