Analyzing Social Media Data in R

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Start Course for Free
4 Hours16 Videos57 Exercises3,413 Learners
4700 XP

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies


Course Description

Analyzing data from social media can provide you with valuable insights. It can inform campaign strategies, improve marketing and sales, measure customer engagement, perform competitor analysis, and identify untapped networks. In this course, you’ll use R to extract and visualize Twitter data, perform network analysis, and view the geolocation of tweets. You’ll use a variety of datasets to put what you’ve learned into play, including tweets about celebrities, technology companies, trending topics, and sports.

  1. 1

    Understanding Twitter data

    Free

    Get started with understanding the power of Twitter data and what you can achieve using social media analysis. In this chapter, you’ll extract your first set of tweets using the Twitter API and functions from the powerful ‘rtweet’ library. Then it’s time to explore how you can use the components from your extracted Twitter data to derive insights for social media analysis.

    Play Chapter Now
    Analyzing twitter data
    50 xp
    Power of twitter data
    100 xp
    Pros and cons of twitter data
    50 xp
    Extracting twitter data
    50 xp
    Prerequisites to set up the R environment
    50 xp
    Search and extract tweets
    100 xp
    Search and extract timelines
    100 xp
    Components of twitter data
    50 xp
    User interest and tweet counts
    100 xp
    Compare follower count
    100 xp
    Retweet counts
    100 xp
  2. 3

    Visualize Tweet texts

    A picture is worth a thousand words! In this chapter, you’ll discover how you can visualize text from tweets using bar plots and word clouds. You’ll learn how to process tweet text and prepare a clean text corpus for analysis. Imagine being able to extract key discussion topics and people's perceptions about a subject or brand from the tweets they are sharing. You’ll be able to do just that using topic modeling and sentiment analysis.

    Play Chapter Now
  3. 4

    Network Analysis and putting Twitter data on the map

    Twitter users tweet, like, follow, and retweet creating complex network structures. In this final chapter, you’ll learn how to analyze these network structures and visualize the relationships between these individual people as a retweet network. By extracting geolocation data from the tweets you’ll also discover how to display tweet locations on a map, and answer powerful questions such as which states or countries are talking about your brand the most? Geographic data adds a new dimension to your Twitter data analysis.

    Play Chapter Now

In the following tracks

Marketing Analytics

Collaborators

Adel NehmeAnneleen Beckers

Prerequisites

Introduction to R
Sowmya Vivek Headshot

Sowmya Vivek

Data Science Coach

Sowmya is an independent consultant and data science coach in Machine Learning, Deep Learning & NLP. She has worked on different projects across Deep learning and NLP, business intelligence, workflow optimization, and e-learning content development. She is a visiting faculty for executive programs on business analytics and NLP. She loves blogging on data science and NLP. Check out her blog at Sowmya Vivek.
See More
Vivek Vijayaraghavan Headshot

Vivek Vijayaraghavan

Data Science Coach

Vivek is a data science trainer and consultant in analytics specializing in the areas of machine learning and natural language processing. He is a certified six sigma black belt and business analytics professional and has over two decades of experience across content management, operations, process excellence, analytics, and business intelligence.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA