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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.
Understanding Twitter dataFree
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.Analyzing twitter data50 xpPower of twitter data100 xpPros and cons of twitter data50 xpExtracting twitter data50 xpPrerequisites to set up the R environment50 xpSearch and extract tweets100 xpSearch and extract timelines100 xpComponents of twitter data50 xpUser interest and tweet counts100 xpCompare follower count100 xpRetweet counts100 xp
Analyzing Twitter data
It’s time to go deeper. Learn how you can apply filters to tweets and analyze Twitter user data using the golden ratio and the Twitter lists they subscribe to. You’ll also learn how to extract trending topics and analyze Twitter data over time to identify interesting insights.Filtering tweets50 xpFiltering for original tweets100 xpFiltering on tweet language100 xpFilter based on tweet popularity100 xpTwitter user analysis50 xpExtract user information100 xpExplore users based on the golden ratio100 xpSubscribers to twitter lists100 xpTwitter trends50 xpAvailable trends50 xpTrends by country name100 xpTrends by city and most tweeted trends100 xpPlotting twitter data over time50 xpVisualizing frequency of tweets100 xpCreate time series objects100 xpCompare tweet frequencies for two brands100 xp
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.Processing twitter text50 xpRemove URLs and characters other than letters100 xpBuild a corpus and convert to lowercase100 xpRemove stop words and additional spaces100 xpVisualize popular terms50 xpRemoving custom stop words100 xpVisualize popular terms with bar plots100 xpWord clouds for visualization100 xpTopic modeling of tweets50 xpThe LDA algorithm50 xpCreate a document term matrix100 xpCreate a topic model100 xpTwitter sentiment analysis50 xpExtract sentiment scores100 xpPerform sentiment analysis100 xp
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.Twitter network analysis50 xpPreparing data for a retweet network100 xpCreate a retweet network100 xpNetwork centrality measures50 xpCalculate out-degree scores100 xpCompute the in-degree scores100 xpCalculate the betweenness scores100 xpVisualizing twitter networks50 xpCreate a network plot with attributes100 xpNetwork plot based on centrality measure100 xpFollower count to enhance the network plot100 xpPutting twitter data on the map50 xpExtract geolocation coordinates100 xpTwitter data on the map100 xpCourse wrap-up50 xp
In the following tracksMarketing Analytics
PrerequisitesIntroduction to R
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.
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.
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