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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with 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 Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Chapter 11.  Working with Twitter Data

In this chapter, we will see how to acquire data from Twitter, exploring the fundamental elements of interaction such as retweets, likes, and trending topics. Initially, we introduce the Twitter API with Python. Then we will distinguish the basic elements of the Twython library and the use of OAuth as an authentication process. Finally, we will perform data acquisition in real time, applying data streaming.

In this chapter, we will cover:

  • The anatomy of Twitter data
  • Using OAuth to access the Twitter API
  • Getting started with Twython
  • Streaming API

With the proliferation of social network sites, we can see what people are talking about in real time and on a large scale. However, we need to be cautious because social networks tend to be noisy; that is why in this case we will need as much data as we can get in order to obtain a true representation of what people think.

Mining Twitter is one of the best ways to find out what people are talking about...

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