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Introduction to Text Analysis in R

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4 hours
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. 1

    Wrangling Text

    Free

    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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    Text as data
    50 xp
    Airline tweets data
    100 xp
    Grouped summaries
    100 xp
    Counting categorical data
    50 xp
    Counting user types
    100 xp
    Summarizing user types
    100 xp
    Tokenizing and cleaning
    50 xp
    Tokenizing and counting
    100 xp
    Cleaning and counting
    100 xp
  2. 3

    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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In the following tracks

Marketing AnalyticsText Mining

Collaborators

Collaborator's avatar
Chester Ismay
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Sumedh Panchadhar
Maham Khan HeadshotMaham Khan

Senior Data Scientist, YouView TV

Maham is a Data Scientist on a mission to make data skills accessible for everyone. She's worked on creating toolkits and exploring experimental applications of data science for urban analytics, disaster risk management, and climate change mitigation at the World Bank. She has a background in Experimental Psychology and Philosophy from the University of Oxford and Urban Data Science from NYU.
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