Python social media analytics analyze and visualize data from Twitter, YouTube, GitHub, and more
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 sto...
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| Format: | eBook |
| Language: | English |
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Birmingham
Packt Publishing
2017
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Brief introduction to machine learningTechniques 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 the APIBasic 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; Summary; 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
- Connecting to social network platforms without OAuthOAuth1 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; Selecting the endpoint; Connect to the API; GitHub; Obtaining OAuth tokens programmatically; Selecting the endpoint; Connecting to the API; YouTube; Creating an application and obtaining an access token programmatically; Selecting the endpoint; Connecting to the API; Pinterest; Creating an application; Selecting the endpoint
- Extracting verbatims for keywordsUser keywords; Brand posts; User hashtags; Noun phrases; Brand posts; User comments; Detecting trends in time series; Maximum shares; Brand posts; User comments; Maximum likes; Brand posts; 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?; Summary; Chapter 4: Analyzing Twitter Using Sentiment Analysis and Entity Recognition; Scope and process; Getting the data; Getting Twitter API keys; Data extraction
- Cover; Copyright; Credits; About the Authors; Acknowledgments; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of 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