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Introduction to Text Analysis in R

IntermediateSkill Level
4.8+
59 reviews
Updated 03/2023
Analyze text data in R using the tidy framework.
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RData Manipulation4 hr15 videos46 Exercises3,850 XP26,923Statement of Accomplishment

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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.

Prerequisites

Introduction to the Tidyverse
1

Wrangling Text

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.
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2

Visualizing Text

3

Sentiment Analysis

4

Topic Modeling

Introduction to Text Analysis in R
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*4.8
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  • іра
    3 days ago

    грейт

  • JHON ALEJANDRO
    2 weeks ago

  • Ondřej
    6 weeks ago

    Intresting topic

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  • Axel Aron
    2 months ago

"грейт"

іра

"Intresting topic"

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Mohammed

FAQs

What R packages does this course use for text analysis?

You use tidy text tools compatible with the tidyverse ecosystem, along with ggplot2 for visualization. The course follows a tidy data approach to text analysis throughout.

Does the course cover sentiment analysis?

Yes. Chapter 3 is dedicated to sentiment analysis, where you move beyond word counts to analyze the emotional valence of text using sentiment lexicons and scoring methods.

What is topic modeling and is it included?

Topic modeling uncovers hidden themes in a collection of documents. Chapter 4 teaches you latent Dirichlet allocation, a standard topic model, to discover underlying topics in text data.

Is this course suitable for someone new to working with text data?

Yes. It is a beginner-level course that starts with the basics of tokenizing and cleaning text, then builds up to sentiment analysis and topic modeling step by step.

What types of text data will I work with?

You work with real-world unstructured text datasets relevant to marketing analytics and other applications, learning to wrangle, visualize, and model text throughout the exercises.

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