Python social media analytics : analyze and visualize data from Twitter, YouTube, GitHub, and more / Siddhartha Chatterjee, Michal Krystyanczuk.

Format:

Book

Author/Creator:

Chatterjee, Siddhartha (Professor of political science), author.

Krystyanczuk, Michal, author.

Language:

English

Subjects (All):

Python (Computer program language).

Data mining.

Social media.

Physical Description:

1 online resource (vi, 292 pages) : illustrations.

Place of Publication:

Birmingham : Packt, 2017.

System Details:

text file

Summary:

"Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book* Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more* Analyze and extract actionable insights from your social data using various Python tools* A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn* Understand the basics of social media mining* Use PyMongo to clean, store, and access data in MongoDB* Understand user reactions and emotion detection on Facebook* Perform Twitter sentiment analysis and entity recognition using Python* Analyze video and campaign performance on YouTube* Mine popular trends on GitHub and predict the next big technology* Extract conversational topics on public internet forums* Analyze user interests on Pinterest* Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.-- Provided by publisher.

Contents:

Preface

Chapter 1: Introduction to the Latest Social Media Landscape and Importance

Introducing social graph

Notion of influence

Social impacts

Platforms on platform

Delving into social data

Understanding semantics

Defining the semantic web

Exploring social data applications

Understanding the process

Working environment

Defining Python

Selecting an IDE

Illustrating Git

Getting the data

Defining API

Scraping and crawling

Analyzing the data

Brief introduction to machine learning

Techniques for social media analysis

Setting up data structure libraries

Visualizing the data

Getting started with the toolset

Summary

Chapter 2: Harnessing Social Data - Connecting, Capturing, and Cleaning

APIs in a nutshell

Different types of API

RESTful API

Stream API

Advantages of social media APIs

Limitations of social media APIs

Connecting principles of APIs

Introduction to authentication techniques

What is OAuth?

User authentication

Application authentication

Why do we need to use OAuth?

Connecting to social network platforms without OAuth

OAuth1 and OAuth2

Practical usage of OAuth

Parsing API outputs

Twitter

Creating application

Selecting the endpoint

Using requests to connect

Facebook

Creating an app and getting an access token

Connect to the API

GitHub

Obtaining OAuth tokens programmatically

Connecting to the API

YouTube

Creating an application and obtaining an access token programmatically

Pinterest

Creating an application.

Selecting the endpoint

Basic cleaning techniques

Data type and encoding

Structure of data

Pre-processing and text normalization

Duplicate removal

MongoDB to store and access social data

Installing MongoDB

Setting up the environment

Starting MongoDB

MongoDB using Python

Chapter 3: Uncovering Brand Activity, Popularity, and Emotions on Facebook

Facebook brand page

The Facebook API

Project planning

Scope and process

Data type

Analysis

Step 1 - data extraction

Step 2 - data pull

Step 3 - feature extraction

Step 4 - content analysis

Keywords

Extracting verbatims for keywords

User keywords

Brand posts

User hashtags

Noun phrases

User comments

Detecting trends in time series

Maximum shares

Maximum likes

Comments

Uncovering emotions

How to extract emotions?

Introducing the Alchemy API

Connecting to the Alchemy API

Setting up an application

Applying Alchemy API

How can brands benefit from it?

Chapter 4: Analyzing Twitter Using Sentiment Analysis and Entity Recognition

Getting Twitter API keys

Data extraction

REST API Search endpoint

Rate Limits

Streaming API

Data pull

Data cleaning

Sentiment analysis

Customized sentiment analysis

Labeling the data

Creating the model

Model performance evaluation and cross-validation

Confusion matrix

K-fold cross-validation

Named entity recognition

Installing NER

Combining NER and sentiment analysis

Chapter 5: Campaigns and Consumer Reaction Analytics on YouTube - Structured and Unstructured

How to get a YouTube API key

Data processing.

Data analysis

Sentiment analysis in time

Sentiment by weekday

Comments in time

Number of comments by weekday

Chapter 6: The Next Great Technology - Trends Mining on GitHub

Connection to GitHub

Data processing

Textual data

Numerical data

Data analysis

Top technologies

Programming languages

Programming languages used in top technologies

Top repositories by technology

Comparison of technologies in terms of forks, open issues, size, and watchers count

Forks versus open issues

Forks versus size

Forks versus watchers

Open issues versus Size

Open issues versus Watchers

Size versus watchers

Chapter 7: Scraping and Extracting Conversational Topics on Internet Forums

Introduction to scraping

Scrapy framework

How it works

Related tools

Creating a project

Creating spiders

Teamspeed forum spider

Data pull and pre-processing

Part-of-speech extraction

Introduction to topic models

Latent Dirichlet Allocation

Applying LDA to forum conversations

Topic interpretation

Chapter 8: Demystifying Pinterest through Network Analysis of Users Interests

Pinterest API

Step 1 - creating an application and obtaining app ID and app secret

Step 2 - getting your authorization code (access code)

Step 3 - exchanging the access code for an access token

Step 4 - testing the connection

Getting Pinterest API data

Scraping Pinterest search results

Building a scraper with Selenium

Scraping time constraints

Pinterest API data

Bigram extraction

Building a graph

Pinterest search results data.

Bigram extraction

Understanding relationships between our own topics

Finding influencers

Conclusions

Community structure

Chapter 9: Social Data Analytics at Scale - Spark and Amazon Web Services

Different scaling methods and platforms

Parallel computing

Distributed computing with Celery

Celery multiple node deployment

Distributed computing with Spark

Text mining With Spark

Topic models at scale

Spark on the Cloud - Amazon Elastic MapReduce

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed August 25, 2017).

ISBN:

9781787126756 (electronic book)

OCLC:

1001253544