
Learning Social Media Analytics with R
Description
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- [*] 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.
Book DescriptionThe 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. 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
Who this book is forIt 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.
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Persons
Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.Ganapathy Karthik :
Contacted for ReviewingBali Raghav :
Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.Sharma Tushar :
Tushar Sharma has a master's degree specializing in data science from the International Institute of Information Technology, Bangalore. He works as a data scientist with Intel. In his previous job he used to work as a research engineer for a financial consultancy firm. His work involves handling big data at scale generated by the massive infrastructure at Intel. He engineers and delivers end to end solutions on this data using the latest machine learning tools and frameworks. He is proficient in R, Python, Spark, and mathematical aspects of machine learning among other things. Tushar has a keen interest in everything related to technology. He likes to read a wide array of books ranging from history to philosophy and beyond. He is a running enthusiast and likes to play badminton and tennis.
Content
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Getting started with data dumps
- Data Science and StackExchange
- Demographics and data science
- Challenges
- Summary
- Chapter 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
- Chapter 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
- 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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File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our eBook Help page.