Introduction to Text Analysis in R
Analyze text data in R using the tidy framework.
- 4 hours
- 15 Videos
- 46 Exercises
- 5,840 Participants
- 3,850 XP
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Course Description
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.
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1
Wrangling Text
FreeSince 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.
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Sentiment Analysis
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.
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Visualizing 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.
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Topic Modeling
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.
-
1
Wrangling Text
FreeSince 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.
-
Visualizing 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.
-
Sentiment Analysis
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.
-
Topic Modeling
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.
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Marc Dotson
Assistant Professor of Marketing, BYU Marriott School of Business
Marc's research is focused on applications of Bayesian inference in marketing, including choice modeling and text analysis. He teaches courses in survey research, conjoint analysis, marketing analytics, and statistical modeling.
See MoreCollaborators
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Chester Ismay
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Sumedh Panchadhar