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Learning Social Media Analytics with R电子书

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作       者:Raghav Bali,Dipanjan Sarkar,Tushar Sharma

出  版  社:Packt Publishing

出版时间:2017-05-26

字       数:393.4万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book ? A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data ? Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. ? Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn ? Learn how to tap into data from diverse social media platforms using the R ecosystem ? Use social media data to formulate and solve real-world problems ? Analyze user social networks and communities using concepts from graph theory and network analysis ? Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels ? Understand the art of representing actionable insights with effective visualizations ? Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on ? Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.
目录展开

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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