Different approaches to organizing and analyzing data of the text variety (books, articles, documents). A primer into regular expressions and ways to effectively search for common patterns in text is also provided.

Different approaches to organizing and analyzing data of the text variety (books, articles, documents).

with R

Text Mining

Since text is unstructured data, a certain amount of wrangling is required to get it into a form where you can analyze it. In this chapter, you will learn how to add structure to text by tokenizing, cleaning, and treating text as categorical data.

Wrangling Text

While counts are nice, visualizations are better. In this chapter, you will learn how to apply what you know from ggplot2 to tidy text data.

Visualizing Text

While word counts and visualizations suggest something about the content, we can do more. In this chapter, we move beyond word counts alone to analyze the sentiment or emotional valence of text.

Sentiment Analysis

In this final chapter, we move beyond word counts to uncover the underlying topics in a collection of documents. We will use a standard topic model known as latent Dirichlet allocation.

Topic Modeling

Airline tweets

Roomba reviews

From social media to product reviews, text is an increasingly important type of data across applications, including marketing analytics. In many instances, text is replacing other forms of unstructured data due to how inexpensive and current it is. However, to take advantage of everything that text has to offer, you need to know how to think about, clean, summarize, and model text. In this course, you will use the latest tidy tools to quickly and easily get started with text. You will learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.

Introduction to the Tidyverse

Analyze text data in R using the tidy framework.

Introduction to Text Analysis in R

Data Manipulation

You'll start with some basics: how to enter strings in R, how to control how numbers are transformed to strings, and finally how to combine strings together to produce output that combines text and nicely formatted numbers.

String basics

Time to meet stringr! You'll start by learning about some stringr functions that are very similar to some base R functions, then how to detect specific patterns in strings, how to split strings apart and how to find and replace parts of strings.

Introduction to stringr

In this chapter you'll learn about regular expressions, a language for describing patterns in strings.  By combining regular expressions with the stringr functions you'll greatly increase your power to manipulate strings.

Pattern matching with regular expressions

Now for two advanced ways to use regular expressions along with stringr: selecting parts of a match (a.k.a capturing) and referring back to parts of a match (a.k.a back-referencing).  You'll also learn to deal with and strings or patterns that contain Unicode characters (e.g. é).

More advanced matching and manipulation

Practice your string manipulation skills on a couple of case studies.  You'll also learn a few new skills, reading strings into R and handling problems of case (e.g. A versus a).

Case studies

DNA sequences from the genome of Yersinia pestis

Narratives

Adverbs

Importance of being earnest

Cat-related accidents

Character strings can turn up in all stages of a data science project. You might have to clean messy string input before analysis, extract data that is embedded in text or automatically turn numeric results into a sentence to include in a report. Perhaps the strings themselves are the data of interest, and you need to detect and match patterns within them. This course will help you master these tasks by teaching you how to pull strings apart, put them back together and use stringr to detect, extract, match and split strings using regular expressions, a powerful way to express patterns.

Intermediate R

In this course you will learn how to pull character strings apart, put them back together and use the stringr package.

String Manipulation with stringr in R

Learn how to pull character strings apart, put them back together and use the stringr package.

Programming

In this chapter, you'll learn the basics of using the bag of words method for analyzing text data.

Jumping into text mining with bag of words

This chapter will teach you how to visualize text data in a way that's both informative and engaging.

Word clouds and more interesting visuals

In this chapter, you'll learn more basic text mining techniques based on the bag of words method.

Adding to your tm skills

This chapter ties everything together with a case study in text mining for HR analytics.

Battle of the tech giants for talent

Coffee tweets

Chardonnay tweets

Anonymous online reviews: Amazon

Anonymous online reviews: Google

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

Learn the Bag of Words technique for Text Mining with R. Start the interactive R tutorial and get started!

Text Mining with Bag-of-Words in R

Learn the bag of words technique for text mining with R.

Machine Learning

In the first chapter, you will learn how to apply qdap's sentiment function called <a href=https://www.rdocumentation.org/packages/qdap/topics/polarity> polarity() </a>. 

Fast & dirty: Polarity scoring

In the second chapter you will explore 3 subjectivity lexicons from tidytext.  Then you will do an inner join to score some text.

Sentiment analysis the tidytext way

Make compelling visuals with your sentiment output.

Visualizing sentiment

Is your property a good rental?  What do people look for in a good rental?

Case study: Airbnb reviews

Line by line polarity for 4 books

4 books as a tidy data frame

4 books as DocumentTermMatrices

Polarity scores of Boston Airbnb reviews

Housing rental reviews from Airbnb in Boston

Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You will end the course by applying your sentiment analysis skills to Airbnb reviews to learn what makes for a good rental.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

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