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Learning Social Media Analytics with R
Table of Contents
Learning Social Media Analytics with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Getting Started with R and Social Media Analytics
Understanding social media
Advantages and significance
Disadvantages and pitfalls
Social media analytics
A typical social media analytics workflow
Data access
Data processing and normalization
Data analysis
Insights
Opportunities
Challenges
Getting started with R
Environment setup
Data types
Data structures
Vectors
Arrays
Matrices
Lists
DataFrames
Functions
Built-in functions
User-defined functions
Controlling code flow
Looping constructs
Conditional constructs
Advanced operations
apply
lapply
sapply
tapply
mapply
Visualizing data
Next steps
Getting help
Managing packages
Data analytics
Analytics workflow
Machine learning
Machine learning techniques
Supervised learning
Unsupervised learning
Text analytics
Summary
2. Twitter – What's Happening with 140 Characters
Understanding Twitter
APIs
Registering an application
Connecting to Twitter using R
Extracting sample Tweets
Revisiting analytics workflow
Trend analysis
Sentiment analysis
Key concepts of sentiment analysis
Subjectivity
Sentiment polarity
Opinion summarization
Features
Sentiment analysis in R
Follower graph analysis
Challenges
Summary
3. Analyzing Social Networks and Brand Engagements with Facebook
Accessing Facebook data
Understanding the Graph API
Understanding Rfacebook
Understanding Netvizz
Data access challenges
Analyzing your personal social network
Basic descriptive statistics
Analyzing mutual interests
Build your friend network graph
Visualizing your friend network graph
Analyzing node properties
Degree
Closeness
Betweenness
Analyzing network communities
Cliques
Communities
Analyzing an English football social network
Basic descriptive statistics
Visualizing the network
Analyzing network properties
Diameter
Page distances
Density
Transitivity
Coreness
Analyzing node properties
Degree
Closeness
Betweenness
Visualizing correlation among centrality measures
Eigenvector centrality
PageRank
HITS authority score
Page neighbours
Analyzing network communities
Cliques
Communities
Analyzing English Football Club's brand page engagements
Getting the data
Curating the data
Visualizing post counts per page
Visualizing post counts by post type per page
Visualizing average likes by post type per page
Visualizing average shares by post type per page
Visualizing page engagement over time
Visualizing user engagement with page over time
Trending posts by user likes per page
Trending posts by user shares per page
Top influential users on popular page posts
Summary
4. Foursquare – Are You Checked in Yet?
Foursquare – the app and data
Foursquare APIs – show me the data
Creating an application – let me in
Data access – the twist in the story
Handling JSON in R – the hidden art
Getting category data – introduction to JSON parsing and data extraction
Revisiting the analytics workflow
Category trend analysis
Getting the data – the usual hurdle
The required end point
Getting data for a city – geometry to the rescue
Analysis – the fun part
Basic descriptive statistics – the usual
Recommendation engine – let's open a restaurant
Recommendation engine – the clichés
Framing the recommendation problem
Building our restaurant recommender
The sentimental rankings
Extracting tips data – the go to step
The actual data
Analysis of tips
Basic descriptive statistics
The final rankings
Venue graph – where do people go next?
Challenges for Foursquare data analysis
Summary
5. Analyzing Software Collaboration Trends I – Social Coding with GitHub
Environment setup
Understanding GitHub
Accessing GitHub data
Using the rgithub package for data access
Registering an application on GitHub
Accessing data using the GitHub API
Analyzing repository activity
Analyzing weekly commit frequency
Analyzing commit frequency distribution versus day of the week
Analyzing daily commit frequency
Analyzing weekly commit frequency comparison
Analyzing weekly code modification history
Retrieving trending repositories
Analyzing repository trends
Analyzing trending repositories created over time
Analyzing trending repositories updated over time
Analyzing repository metrics
Visualizing repository metric distributions
Analyzing repository metric correlations
Analyzing relationship between stargazer and repository counts
Analyzing relationship between stargazer and fork counts
Analyzing relationship between total forks, repository count, and health
Analyzing language trends
Visualizing top trending languages
Visualizing top trending languages over time
Analyzing languages with the most open issues
Analyzing languages with the most open issues over time
Analyzing languages with the most helpful repositories
Analyzing languages with the highest popularity score
Analyzing language correlations
Analyzing user trends
Visualizing top contributing users
Analyzing user activity metrics
Summary
6. Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange
Understanding StackExchange
Data access
The StackExchange data dump
Accessing data dumps
Contents of data dumps
Quick overview of the data in data dumps
Posts
Users
Getting started with data dumps
Data Science and StackExchange
Demographics and data science
Challenges
Summary
7. Believe What You See – Flickr Data Analysis
A Flickr-ing world
Accessing Flickr's data
Creating the Flickr app
Connecting to R
Getting started with Flickr data
Understanding Flickr data
Understanding more about EXIF
Understanding interestingness – similarities
Finding K
Elbow method
Silhouette method
Are your photos interesting?
Preparing the data
Building the classifier
Challenges
Summary
8. News – The Collective Social Media!
News data – news is everywhere
Accessing news data
Creating applications for data access
Data extraction – not just an API call
The API call and JSON monster
HTML scraping from the links – the bigger monster
Sentiment trend analysis
Getting the data – not again
Basic descriptive statistics – the usual
Numerical sentiment trends
Emotion-based sentiment trends
Topic modeling
Getting to the data
Basic descriptive analysis
Topic modeling for Mr. Trump's phases
Cleaning the data
Pre-processing the data
The modeling part
Analysis of topics
Summarizing news articles
Document summarization
Understanding LexRank
Summarizing articles with lexRankr
Challenges to news data analysis
Summary
Index
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