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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2017
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Table of Contents
Learning Social Media Analytics with R
Learning Social Media Analytics with R
Copyright © 2017 Packt Publishing
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First published: May 2017
Production reference: 1220517
Published by Packt Publishing Ltd.
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ISBN 978-1-78712-752-4
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Credits
Authors
Raghav Bali
Dipanjan Sarkar
Tushar Sharma
Reviewer
Karthik Ganapathy
Commissioning Editor
Amey Varangaonkar
Acquisition Editor
Tushar Gupta
Content Development Editor
Amrita Noronha
Technical Editor
Akash Patel
Copy Editors
Vikrant Phadkay
Safis Editing
Project Coordinator
Shweta H Birwatkar
Proofreader
Safis Editing
Indexer
Pratik Shirodkar
Graphics
Tania Dutta
Production Coordinator
Shantanu Zagade
Cover Work
Shantanu Zagade
About the Author
Raghav Bali has a master's degree (gold medalist) in information technology from International Institute of Information Technology, Bangalore. He is a data scientist at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development to develop scalable machine learning-based solutions. He has worked as an analyst and developer in domains such as ERP, finance, and BI with some of the top companies of the world.
Raghav is a technology enthusiast who loves reading and playing around with new gadgets and technologies. He recently co-authored a book on machine learning titled R Machine Learning by Example, Packt Publishing. He is a shutterbug, capturing moments when he isn't busy solving problems.
I would like to express my gratitude to my family, teachers, friends, colleagues and mentors who have encouraged, supported and taught me over the years. I would also like to take this opportunity to thank my co-authors and good friends Dipanjan Sarkar and Tushar Sharma, who made this project a memorable and enjoyable one.
I would like to thank Tushar Gupta, Amrita Noronha, Akash Patel, and Packt for the opportunity and their support throughout this journey. Last but not least, thanks to the R community for the amazing stuff that they do!
Dipanjan Sarkar is a data scientist at Intel, the world's largest silicon company, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in information technology with specializations in data science and software engineering from the International Institute of Information Technology, Bangalore.
Dipanjan has been an analytics practitioner for over 5 years now, specializing in statistical, predictive, and text analytics. He has also authored several books on machine learning and analytics including R Machine Learning by Example and What you need to know about R, Packt. Besides this, he occasionally spends time reviewing technical books and courses. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups and data science. In his spare time he loves reading, gaming, watching popular sitcoms and football.
I am indebted to my parents, partner, friends, and well-wishers for always standing by my side and supporting me in all my endeavors. Your support keeps me going day in and day out to take on new challenges! I would also like to thank my good friends and fellow colleagues, Raghav Bali and Tushar Sharma, for co-authoring and making the experience more enjoyable. Last but never the least, I would like to thank Tushar Gupta, Amrita Noronha, Akash Patel, and Packt for giving me this wonderful opportunity to share my knowledge and experiences with analytics and R enthusiasts out there who are doing truly amazing things every day. And a big thumbs up to the R community for building an excellent analytics ecosystem.
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.
I would like to express my gratitude to my family, teachers and friends who have encouraged, supported and taught me over the years. Special thanks to my classmates, friends, and colleagues, Dipanjan Sarkar and Raghav Bali for co-authoring and making this journey wonderful through their input and eye for detail.
I would like to thank Tushar Gupta, Amrita Noronha, and Packt for the opportunity and their support throughout the journey.
About the Reviewer
Karthik Ganapathy is an analytics professional with over 12 years of professional experience in analytics, predictive modeling, and project management. He has worked with several Fortune 500 clients and helped them derive business value using data.
I would like to thank my wife Sudharsana and my daughter Amrita for being a great support during the period I was reviewing the content.
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Preface
The Internet has truly grown to be humongous, especially in the last decade, with the rise of various forms of social media that give users a platform to express themselves and also communicate and collaborate with each other. The current social media landscape is a complex mesh of social network platforms and applications, catering to specific audiences with unique as well as overlapping features. Each of these social networks are potential gold mines of data which are being (and can be) used to study, leverage and improve our understanding of demographics, behaviors, collaboration, user engagement, branding and so on across different domains and spheres of our lives.
This book will help the reader to understand the current social media landscape and help in understanding how analytics and machine learning can be leveraged to derive insights from social media data. It will enable readers to utilize R and its ecosystem to visualize and analyze data from different social networks. This book will also leverage machine learning, data science and other advanced concepts and techniques to solve real-world use cases spread across diverse social network domains including Twitter, Facebook, GitHub, FourSquare, StackExchange, Flickr, and more.
What this book covers
Chapter 1, Getting Started with R and Social Media Analytics, builds on foundations related to social media platforms and analyzing data relevant to social media. A concise introduction to R is given, including coverage of R syntax, data constructs, and functions. Basic concepts from machine learning, data analytics, and text analytics are also covered, setting the tone for the content in subsequent chapters.
Chapter 2, Twitter – What's Happening with 140 Characters, sets the theme for social media analytics with a focus on Twitter. It leverages R packages to extract and analyze Twitter data to uncover interesting insights through multiple use-cases, involving machine learning techniques such as trend analysis, sentiment analysis, clustering, and social graph analysis.
Chapter 3, Analyzing Social Networks and Brand Engagements with Facebook, focuses on analyzing data from perhaps the most popular social network in the world—Facebook! Readers will learn how to use the Graph API to retrieve data as well as use frameworks such as Netvizz to extract brand page data. Techniques to analyze personal social networks will be covered in detail. Besides this, readers will gain conceptual knowledge about social network analysis and graph theory. This knowledge will be used in action by analyzing a huge network of football brand pages to understand relationships, page engagement, and popularity.
Chapter 4, Foursquare – Are You Checked in Yet?, targets the popular social media channel Foursquare. Readers will learn how to collect this data using the Foursquare APIs. Steps for visualizing and analyzing this data will be depicted to uncover insights into user behavior. This data will be used to define and solve some analytics use-cases, which include sentiment analysis, graph analytics, and much more.
Chapter 5, Analyzing Software Collaboration Trends I – Social Coding with GitHub, introduces the popular social coding and collaboration platform GitHub for analyzing software collaboration trends. Readers will gain insights into using the GitHub API from R to extract useful data pertaining to users and repositories. Detailed analyzes of repository activity, repository trends, language trends, and user trends will be presented with real-world examples.
Chapter 6, Analyzing Software Collaboration Trends II – Answering Your Questions with StackExchange, introduces the StackExchange platform through its data organization and access methods. Readers learn and uncover interesting collaboration, demographic, and other patterns through use cases which leverage visualizations and different analysis techniques learned in previous chapters.
Chapter 7, Believe What You See – Flickr Data Analysis, presents Flickr through its APIs and uses some amazing packages such as piper, dplyr, and so on to extract data and insights from some complex data formats. The chapter also leverages machine learning concepts like clustering and classification to better understand Flickr.
Chapter 8, News – The Collective Social Media!, deals with analysis of free and unstructured text. Readers will learn how to collect news data from web sources using methodologies like scraping. The basic analysis on the textual data will consist of various statistical measures. Readers will also gain hands-on knowledge on advanced analysis like sentiment analysis, topic modeling, and text summarization on news data based on some interesting use cases.
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Who this book is for
This book is for 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 is not necessary. The book has been written keeping in mind the varying levels of expertise of its readers. It also includes links, pointers, and exercises for intermediate to advanced readers to explore further.
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Chapter 1. Getting Started with R and Social Media Analytics
The invention of computers, digital electronics, social media, and the Internet have truly ushered us from the industrial age into the information age. The Internet, and more specifically the invention of World Wide Web in the early 1990s, helped people to build an inter-connected universal platform where information can be stored, shared and consumed by anyone with an electronic device capable of connecting to the Web. This has led to the creation of vast amounts of information, ideas and opinions which people, brands, organizations and businesses want to share with everyone around the world. So, social media was born which provides interactive platforms to post content, share ideas, messages and opinions about everything under the sun.
This book will take you on a journey to understand various popular social media, analyzing rich data generated by these media and gaining valuable insights. We will focus on social media which cater to audiences in different forms, like micro-blogging, social networking, software collaboration, news and media sharing platforms. The main objective is to use standardized data access and retrieval techniques using social media application programming interfaces (APIs) to gather data from these websites and apply different data mining, statistical and machine learning, and natural language processing techniques on the data by leveraging the R programming language. This book will provide you with the tools, techniques, and approaches which would help you achieve the same. This introductory chapter will cover several important concepts which would help you get a jumpstart on social media analytics. They are mentioned as follows:
We will look at social media, the various forms of social media which exist today, and how it has impacted our society. This will help us understand the entire scope pertaining to social media analytics and the opportunity presented by it which would be valuable for consumers as well as businesses and brands. Concepts related to analytics, machine learning and text analytics coupled with hands on examples depicting the various features of the R programming language will help you get a grip on essential things which are necessary for the rest of this book. Without further delay, let's get started!
Understanding social media
The Internet and the information age have been responsible for revolutionizing the way we humans interact with each other in the 21st Century. Almost everyone uses some form of electronic communication, be it a laptop, tablet, smartphone or a personal computer. Social media is built upon the concept of platforms where people use computer-mediated communication (CMC) methods to communicate with others. This can range from instant messaging, emails, and chat rooms to social forums and social networking. To understand social media, you need to understand the origins of legacy or traditional media which gradually evolved into social media. Entities like the popular television, newspapers, radio, movies, books and magazines are various ways of sharing and consuming information, ideas and opinions. It's important to remember that social media has not replaced the older legacy based media; they co-exist peacefully together as we use and consume them both in our day-to-day lives.
Legacy media typically follow a one-way communication system. For instance, I can always read a magazine or watch a show on the television or get updated about the news from newspapers, but I cannot voice my opinions or share my ideas using the same media instantly. The communication mechanism in the various forms of social media is a two-way street, where audiences can share information and ideas and others can consume them and voice their own ideas, opinions and feedback on the same, and even share their own content based on what they see. Legacy based media, like radio or television, now use social media to provide a two-way communication mechanism to support their communications, but it's much more seamless in social media where anyone and everyone can share content, communicate with others, freely voice their ideas and opinions on a huge scale.
We can now formally define social media as interactive applications or platforms based on the principles of Web 2.0 and computer-mediated communication, which enable users to be publishers as well as consumers, to create and share ideas, opinions, information, emotions and expressions in various forms. While different and diverse forms of social media exist, they have several key features in common which are mentioned briefly as follows:
Indeed social media give users their own unique identity and the freedom to express themselves in their own user profiles. These profiles are maintained as accounts by social media companies. Features like what you see is what you get (WYSIWYG) editors, emoticons, photos and videos help users in creating and sharing rich content. Social networking capabilities enables users to add other users to their own friend or contact lists and create groups and forums where they can share and talk about like-minded interests. The following figure shows us some of the popular social media used today across the globe:
I am sure you recognize several of these popular social media from their logos, which you must have seen on your own smartphone or on the web. Social media is used in various ways and media can be grouped into distinct buckets by the nature of its usage and its features. We mention several popular social media in the following points, some of which we will be analyzing in the future chapters:
This list is not an exhaustive list of social media because there are so many applications and platforms out there. We apologize in advance if we missed out mentioning your favorite social media! The list should clarify the different forms of communication and content sharing mechanisms that are available for users, and that they can leverage any of these social media to share content and connect with other users. We will now discuss some of the key advantages and significance which social media has to offer.
Advantages and significance
Social media has gained immense popularity and importance so that today almost everyone can't stay away from it. Not only is social media a medium for people to express their views, but also a very powerful tool which can be used by businesses to target new and existing customers and increase revenue. We will discuss some of the main advantages of social media as follows:
The significance and importance of social media is quite evident from the preceding points. In today's interconnected world, social media has almost become indispensable and although it might have a lot of disadvantages, including distractions, if we use it for the right reasons, it can indeed be a very important tool or medium to help us achieve great things.
Disadvantages and pitfalls
Even though we have been blowing the trumpet about social media and its significance, I'm sure you are already thinking about pitfalls and disadvantages, which are directly or indirectly caused from social media. We want to cover all aspects of social media including the good and the bad, so let's look at some negative aspects:
We have discussed several pitfalls attached to using social media and some of them are very serious concerns. Proper social media usage guidelines and policies should be borne in mind by everyone because social media is like a magnifying glass: anything you post can be used against you or can potentially prove harmful later. Be it extremely sensitive personal information, or confidential information, like design plans for your next product launch, always think carefully before sharing anything with the rest of the world.
However, if you know what you are doing, social media can definitely be used as a proper tool for your personal as well as professional gain.
Social media analytics
We now have a detailed overview of social media, its significance, pitfalls, and various facets. We will now discuss social media analytics and the benefits it offers for data analysts, scientists and businesses in general looking to gather useful insights from social media. Social media analytics, also known as social media mining or social media intelligence, can be defined as the process of gathering data (usually unstructured) from social media platforms and analyzing the data using diverse analytical techniques to extract vital insights, which can be used to make data-driven business decisions. There are lots of opportunities and challenges involved in social media analytics, which we will be discussing in further detail in later sections. An important thing to remember is that the processes involved in social media analytics are usually domain-agnostic and you can apply them on data belonging to any organization or business in any domain.
The most important step in going forward with any social media analytics based workflow or process is to determine the business goals or objectives and the insights that we want to gather from our analyzes. These goals are usually in the form of key performance indicators (KPIs). For instance, the total number of followers, number of likes and shares can be KPIs to measure brand engagement with customers using social media. Sometimes data is not structured and the end objectives are not very concrete. Techniques like natural language processing and text analytics can be leveraged in such cases to extract insights from noisy unstructured text data like understanding the sentiment or mood of customers for a particular service or product and trying to understand the key trends and themes based on customer tweets or posts at any point in time.
A typical social media analytics workflow
We will be analyzing data from diverse social media applications and platforms throughout the course of this book. However, it is essential to have a good grasp of the essential concepts behind any typical analytics process or workflow. While we will be expanding more on data analytics and mining processes later, let us look at a typical social media analytics workflow in the following figure:
From the preceding diagram, we can broadly classify the main steps involved in the analytics workflow as follows:
We will now briefly expand upon each of these four processes since we will be using them extensively in future chapters.
Data access
For access to social media data, you can usually do it using standard data retrieval methods in two ways.
The first technique is to use official APIs provided by the social media platform or organization itself.
The second technique is to use unofficial mechanisms, like web crawling and scraping. An important point to remember is that crawling and scraping social media websites and using that data for commercial purposes, like selling the data to other organizations, is usually against their terms of service. We will therefore not be using such methods in our book. Besides this, we will be following the necessary politeness policies while accessing social media data using their APIs, so that we do not overload them with too many requests. The data we'll obtain is the raw data which can be further processed and normalized as needed.
Data processing and normalization
The raw data obtained from data retrieval using social media APIs may not be structured and clean. In fact most of the data obtained from social media is noisy, unstructured and often contains unnecessary tokens such as Hyper Text Markup Language (HTML) tags and other metadata. Usually, data streams from social media APIs have JavaScript Object Notation (JSON) response objects, which consist of key value pairs just like the example shown in the following snippet:
The preceding JSON object consists of a typical response from the Twitter API showing details of a user profile. Some APIs might return data in other formats, such as Extensible Markup Language (XML) or Comma Separated Values (CSV), and each format needs to be handled properly.
Often social media data contains unstructured textual data which needs additional text pre-processing and normalization before it can be fed into any standard data mining or machine learning algorithm. Text normalization is usually done using several techniques to clean and standardize the text. Some of them are:
More advanced processing can insert additional metadata to describe the text better, such as adding parts of speech (POS) tags, phrase tags, named entity tags, and so on.
Data analysis
This is the core of the whole workflow, where we apply various techniques to analyze the data: this could be the raw native data itself, or the processed and curated data. Usually the techniques used in analysis can be broadly classified into three areas:
Data mining and machine learning have several overlapping concepts, including the fact that both use statistical techniques and try to find patterns from underlying data. Data mining is more about finding key patterns or insights from data; and machine learning is more about using mathematics, statistics, and even some of these data mining algorithms, to build models to predict or forecast outcomes. While both of these techniques need structured and numeric data to work with, more complex analyzes with unstructured textual data is usually handled in the separate realm of text analytics by leveraging natural language processing which enables us to use several tools, techniques and algorithms to analyze free-flowing unstructured text. We will be using techniques, from these three areas to analyze data from various social media platforms throughout this book. We will cover important concepts from data analytics and text analytics briefly towards the end of this chapter.
Insights
The end results from our workflow are the actual insights which act as facts or concrete data points to achieve the objective of the analysis. This can be anything from a business intelligence report to visualizations such as bar graphs, histograms, or even word or phrase clouds. Insights should be crisp, clear, and actionable so that it can be easy for businesses to take valuable decisions in time by leveraging them.
Opportunities
Based on the advantages of social media, we can derive plentiful opportunities which lie within the scope of social media analytics. You can save a lot of cost involved in targeted advertising and promotions by analyzing your social media traffic patterns. You can see how users engage with your brand or business using social media, for instance, when it is the perfect time to share something interesting, such as a new service, product, or even an interesting anecdote about your company. Based on traffic from different geographies, you can analyze and understand the preferences of users from different parts of the world. Users love it if you publish promotions in their local language, and businesses are already leveraging such capabilities from social media platforms such as Facebook to target users in specific countries based on localized content.
The social media analytics landscape is still young and emerging and has a lot of untapped potential.
Let us understand the potential of social media analytics better by taking a real-world example.
Consider you are running a profitable business with active engagement on various social media channels. How can you use the data generated from social media to know how you are doing and how your competitors are doing? Live data streams from Twitter could be continuously analyzed to get real-time mood, sentiment, emotion, and reactions of people to your products and services. You could even analyze the same for your rival competitors to see when they are launching their commodities and how users are reacting to them. With Facebook, you can do the same and even push localized promotions and advertisements to see if they help in generating better revenue. News portals would give you live feeds of trending news articles and insights into the current state of the economy and current events and help you decide if these are favorable times for a thriving business or should you be preparing for some hard times. Sentiment analysis, concept mining, topic models, clustering, and inference are just a few examples of using analytics on social media. The opportunities are huge—you just need to have a clear objective in mind so that you can use analytics effectively to solve that objective.
Challenges
Before we delve into the challenges associated with social media analytics let us look at the following interesting facts:
These statistics give you a rough idea about the massive scale of data being generated and consumed in these social media platforms. This leads to some challenges:
These are perhaps the most prevalent challenges when analyzing social media data, amongst many other challenges, that you might face in your social media analytics journey. Let's now get acquainted with the R programming language, which will be useful to us when we are performing our analyzes.
Data types
There are several basic data types in R for handling different types of data and values:
Common functions for each data type include as and is, which are used for converting data types (typecasting) and checking the data type respectively.
For example, as.numeric(…) would typecast the data or vector indicated by the ellipses into numeric type and is.numeric(…) would check if the data is of numeric type.
Let us look at a few more examples for the various data types in the following code snippet to understand them better:
The preceding examples should make the concepts clearer. Notice that non-zero numeric values are logically TRUE always, and zero values are FALSE, as we can see from typecasting our numeric vector to logical. We will now dive into the various data structures in R.
