Mastering Social Media Mining with R电子书

Mastering Social Media Mining with R

Table of Contents

Mastering Social Media Mining with R

Credits

About the Authors

About the Reviewers

www.PacktPub.com

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Why subscribe?

Free access for Packt account holders

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

Errata

Piracy

Questions

1. Fundamentals of Mining

Social media and its importance

Various social media platforms

Social media mining

Challenges for social media mining

Social media mining techniques

Graph mining

Text mining

The generic process of social media mining

Getting authentication from the social website – OAuth 2.0

Differences between OAuth and OAuth 2.0

Data visualization R packages

The simple word cloud

Sentiment analysis Wordcloud

Preprocessing and cleaning in R

Data modeling – the application of mining algorithms

Opinion mining (sentiment analysis)

Steps for sentiment analysis

Community detection via clustering

Result visualization

An example of social media mining

Summary

2. Mining Opinions, Exploring Trends, and More with Twitter

Twitter and its importance

Understanding Twitter's APIs

Twitter vocabulary

Creating a Twitter API connection

Creating a new app

Finding trending topics

Searching tweets

Twitter sentiment analysis

Collecting tweets as a corpus

Cleaning the corpus

Estimating sentiment (A)

Estimating sentiment (B)

Summary

3. Find Friends on Facebook

Creating an app on the Facebook platform

Rfacebook package installation and authentication

Installation

A closer look at how the package works

A basic analysis of your network

Network analysis and visualization

Social network analysis

Degree

Betweenness

Closeness

Cluster

Communities

Getting Facebook page data

Trending topics

Trend analysis

Influencers

Based on a single post

Based on multiple posts

Measuring CTR performance for a page

Spam detection

Implementing a spam detection algorithm

The order of stories on a user's home page

Recommendations to friends

Reading the output

Other business cases

Summary

4. Finding Popular Photos on Instagram

Creating an app on the Instagram platform

Installation and authentication of the instaR package

Accessing data from R

Searching public media for a specific hashtag

Searching public media from a specific location

Extracting public media of a user

Extracting user profile

Getting followers

Who does the user follow?

Getting comments

Number of times hashtag is used

Building a dataset

User profile

User media

Travel-related media

Who do they follow?

Popular personalities

Who has the most followers?

Who follows more people?

Who shared most media?

Overall top users

Most viral media

Finding the most popular destination

Locations

Locations with most likes

Locations most talked about

What are people saying about these locations?

Most repeating locations

Clustering the pictures

Recommendations to the users

How to do it

Top three recommendations

Improvements to the recommendation system

Business case

Reference

Summary

5. Let's Build Software with GitHub

Creating an app on GitHub

GitHub package installation and authentication

Accessing GitHub data from R

Building a heterogeneous dataset using the most active users

Data processing

Building additional metrics

Exploratory data analysis

EDA – graphical analysis

Which language is most popular among the active GitHub users?

What is the distribution of watchers, forks, and issues in GitHub?

How many repositories had issues?

What is the trend on updating repositories?

Compare users through heat map

EDA – correlation analysis

How Watchers is related to Forks

Correlation with regression line

Correlation with local regression curve

Correlation on segmented data

Correlation between the languages that user's use to code

How to get the trend of correlation?

Reference

Business cases

Summary

6. More Social Media Websites

Searching on social media

Accessing product reviews from sites

Retrieving data from Wikipedia

Using the Tumblr API

Accessing data from Quora

Mapping solutions using Google Maps

Professional network data from LinkedIn

Getting Blogger data

Retrieving venue data from Foursquare

Use cases

Yelp and other networks

Limitations

Summary

Index