Python Social Media Analytics.

Chatterjee, Siddhartha.
1st ed.
Birmingham : Packt Publishing, Limited, 2017.
1 online resource (307 pages)
Python (Computer program language).
Electronic books.
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Table of Contents
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
Chapter 2: Harnessing Social Data - Connecting, Capturing, and Cleaning
APIs in a nutshell
Different types of 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
Creating application
Selecting the endpoint
Using requests to connect
Creating an app and getting an access token
Selecting the endpoint
Connect to the API
Obtaining OAuth tokens programmatically
Selecting the endpoint
Connecting to the API
Creating an application and obtaining an access token programmatically
Selecting the endpoint
Connecting to the API
Creating an application.
Selecting the endpoint
Connecting to the API
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
Step 1 - data extraction
Step 2 - data pull
Step 3 - feature extraction
Step 4 - content analysis
Extracting verbatims for keywords
User 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
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
Scope and process
Getting the data
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
Scope and process
Getting the data
How to get a YouTube API key
Data pull
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
Scope and process
Getting the data
Rate Limits
Connection to GitHub
Data pull
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
Scope and process
Getting the data
Introduction to scraping
Scrapy framework
How it works
Related tools
Creating a project
Creating spiders
Teamspeed forum spider
Data pull and pre-processing
Data cleaning
Part-of-speech extraction
Data analysis
Introduction to topic models
Latent Dirichlet Allocation
Applying LDA to forum conversations
Topic interpretation
Chapter 8: Demystifying Pinterest through Network Analysis of Users Interests
Scope and process
Getting the data
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
Data pull and pre-processing
Pinterest API data
Bigram extraction
Building a graph
Pinterest search results data.
Bigram extraction
Building a graph
Data analysis
Understanding relationships between our own topics
Finding influencers
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
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Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Krystyanczuk, Michal.
Other format:
Print version: Chatterjee, Siddhartha Python Social Media Analytics
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