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Mastering Social Media Mining with Python
Mastering Social Media Mining with Python

Mastering Social Media Mining with Python: Unearth deeper insight from your social media data with advanced Python techniques for acquisition and analysis

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Mastering Social Media Mining with Python

Chapter 2.  #MiningTwitter – Hashtags, Topics, and Time Series

This chapter is about data mining on Twitter. The topics covered in this chapter include the following:

  • Interacting with the Twitter API using Tweepy
  • Twitter data - the anatomy of a tweet
  • Tokenization and frequency analysis
  • Hashtags and user mentions in tweets
  • Time series analysis

Getting started

Twitter is one of the most well-known online social networks that enjoy extreme popularity in the recent years. The service they provide is referred to as microblogging, which is a variant of blogging where the pieces of content are extremely short-in the case of Twitter, there is a limitation of 140 characters like an SMS for each tweet. Different from other social media platforms, such as Facebook, the Twitter network is not bidirectional, meaning that the connections don't have to be mutual: you can follow users who don't follow you back, and the other way round.

Traditional media is adopting social media as a way to reach a wider audience, and most celebrities have a Twitter account to keep in touch with their fans. Users discuss happening events in real time, including celebrations, TV shows, sport events, political elections, and so on.

Twitter is also responsible for popularizing the use of the term hashtag as a way to group conversations and allow users...

The Twitter API

Twitter offers a series of APIs to provide programmatic access to Twitter data, including reading tweets, accessing user profiles, and posting content on behalf of a user.

In order to set up our project to access Twitter data, there are two preliminary steps, as follows:

  • Registering our application
  • Choosing a Twitter API client

The registration step will take a few minutes. Assuming that we are already logged in to our Twitter account, all we need to do is point our browser to the Application Management page at http://apps.twitter.com and create the new app.

Once the app is registered, under the Keys and Access Tokens tab, we can find the information we need to authenticate our application. The Consumer Key and Consumer Secret (also called API Key and API Secret, respectively) are a setting of your application. The Access Token and Access Token Secret are instead a setting for your user account. Your application can potentially ask for access to several users through their...

Collecting data from Twitter

In order to interact with the Twitter APIs, we need a Python client that implements the different calls to the API itself. There are several options as we can see from the official documentation (https://dev.twitter.com/overview/api/twitter-libraries). None of them are officially maintained by Twitter and they are backed by the open source community. While there are several options to choose from, some of them almost equivalent, so we will choose to use Tweepy here as it offers a wider support for different features and is actively maintained.

The library can be installed via pip:

$ pip install tweepy==3.3.0 

Tip

Python 3 compatibility

We're specifically installing version 3.3 of Tweepy, because of an issue with the latest version of Tweepy and Python 3, which prevents running the examples in our Python 3.4 environment. The issue was still unresolved at the time of writing, but it's likely to be fixed soon.

The first part of the interaction with the...

Analyzing tweets - entity analysis

This section is all about analyzing entities in tweets. We're going to perform some frequency analysis using the data collected in the previous section. Slicing and dicing this data will allow users to produce some interesting statistics that can be used to get some insights on the data and answer some questions.

Analyzing entities such as hashtags is interesting as these annotations are an explicit way for the author to label the topic of the tweet.

We start with the analysis of the tweets by Packt Publishing. As Packt Publishing supports and promotes open source software, we are interested in finding what kind of technologies are mentioned often by Packt Publishing.

The following script extracts the hashtags from a user timeline, producing a list of the most common ones:

# Chap02-03/twitter_hashtag_frequency.py 
import sys 
from collections import Counter 
import json 
 
def get_hashtags(tweet): 
  entities = tweet.get('entities', {}) 
  hashtags...

Analyzing tweets - text analysis

The previous section analyzed the entity field of a tweet. This provides useful knowledge on the tweet, because these entities are explicitly curated by the author of the tweet. This section will focus on unstructured data instead, that is, the raw text of the tweet. We'll discuss aspects of text analytics such as text preprocessing and normalization and we'll perform some statistical analysis on the tweets. Before digging the details, we'll introduce some terminology.

Tokenization is one of the important steps in the preprocessing phase. Given a stream of text (such as a tweet status), tokenization is the process of breaking this text down into individual units called tokens. In the simplest form, these units are words, but we could also work on a more complex tokenization that deals with phrases, symbols, and so on.

Tokenization sounds like a trivial task, and it's been widely studied by the natural language processing community. Chapter...

Getting started


Twitter is one of the most well-known online social networks that enjoy extreme popularity in the recent years. The service they provide is referred to as microblogging, which is a variant of blogging where the pieces of content are extremely short-in the case of Twitter, there is a limitation of 140 characters like an SMS for each tweet. Different from other social media platforms, such as Facebook, the Twitter network is not bidirectional, meaning that the connections don't have to be mutual: you can follow users who don't follow you back, and the other way round.

Traditional media is adopting social media as a way to reach a wider audience, and most celebrities have a Twitter account to keep in touch with their fans. Users discuss happening events in real time, including celebrations, TV shows, sport events, political elections, and so on.

Twitter is also responsible for popularizing the use of the term hashtag as a way to group conversations and allow users to follow a...

The Twitter API


Twitter offers a series of APIs to provide programmatic access to Twitter data, including reading tweets, accessing user profiles, and posting content on behalf of a user.

In order to set up our project to access Twitter data, there are two preliminary steps, as follows:

  • Registering our application
  • Choosing a Twitter API client

The registration step will take a few minutes. Assuming that we are already logged in to our Twitter account, all we need to do is point our browser to the Application Management page at http://apps.twitter.com and create the new app.

Once the app is registered, under the Keys and Access Tokens tab, we can find the information we need to authenticate our application. The Consumer Key and Consumer Secret (also called API Key and API Secret, respectively) are a setting of your application. The Access Token and Access Token Secret are instead a setting for your user account. Your application can potentially ask for access to several users through their access...

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Key benefits

  • Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide
  • Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data
  • This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data

Description

Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.

Who is this book for?

This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data.

What you will learn

  • Interact with a social media platform via their public API with Python
  • Store social data in a convenient format for data analysis
  • Slice and dice social data using Python tools for data science
  • Apply text analytics techniques to understand what people are talking about on social media
  • Apply advanced statistical and analytical techniques to produce useful insights from data
  • Build beautiful visualizations with web technologies to explore data and present data products

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Length: 338 pages
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Publication date : Jul 29, 2016
Length: 338 pages
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Table of Contents

9 Chapters
1. Social Media, Social Data, and Python Chevron down icon Chevron up icon
2. #MiningTwitter – Hashtags, Topics, and Time Series Chevron down icon Chevron up icon
3. Users, Followers, and Communities on Twitter Chevron down icon Chevron up icon
4. Posts, Pages, and User Interactions on Facebook Chevron down icon Chevron up icon
5. Topic Analysis on Google+ Chevron down icon Chevron up icon
6. Questions and Answers on Stack Exchange Chevron down icon Chevron up icon
7. Blogs, RSS, Wikipedia, and Natural Language Processing Chevron down icon Chevron up icon
8. Mining All the Data! Chevron down icon Chevron up icon
9. Linked Data and the Semantic Web Chevron down icon Chevron up icon

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Abdulhakim Haliru Dec 15, 2017
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Great book. I ordered it all the way from Nigeria, it arrived and have been very resourceful for me.
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Brian Jun 10, 2017
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Used this book for one of my graduate degree classes. Very well written and its code works flawlessly.
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blastkat Apr 24, 2017
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Purchasing the right gift for the someone you care about on their birthday can be difficult. The second of my three daughters is in the IT field like me. She's very ambitious and an over-achiever. The week before her birthday we were talking on the phone about a business she owns. She was programming an API. She mentioned really wanting a Python book. We bounced ideas around and just talked. Afterward I took her offhand comment and purchased this for her birthday. I had it delivered to her office. She said her co-workers didn't understand why she was so excited over a book. I'm very happy with this purchase.
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777iam Mar 10, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I would like to state first that, I am a seasoned Java/C# software engineer with no experience in Python. I have been reading this book for last 3 days and now I feel really confidence how to apply data mining on social media API. The author provides different options and approaches and tell you which one is better. You will be able to understand how to retrieve real time post or archive ones with efficient code that will boost your productivity.Another important thing to mention, is that the source code provided is updated with current trends of API design and performance. I was able to port my old Java Twitter/Facebook mining engine into Python in just 3 hours. Man, I just feel that I have discovered the holy grail.Just reading the introduction and the chapter you want to master (Twitter, Facebook, Google+, etc), you will have the right arsenal to develop a solid social media mining framework.
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Amazon Customer Oct 20, 2016
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Nice bookIt is very helpful in getting insights of social media through Python programming.
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