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Introduction to Text Classification in R with quanteda
Coursera Project Network

Introduction to Text Classification in R with quanteda

Taught in English

Nicole Baerg

Instructor: Nicole Baerg

Included with Coursera Plus

Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Beginner level

Recommended experience

2 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning

What you'll learn

  • Import text documents, reshape texts from documents to paragraphs, and turn your texts into a machine readable format.

  • Classify presidential concession speeches by political party using a Naive Bayes algorithm and assess the accuracy of the predictions.   

Details to know

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Beginner level

Recommended experience

2 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning

See how employees at top companies are mastering in-demand skills

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Load text documents into R studio, convert a number of text documents into a corpus, and extract data from text document file names and add them to a new column in a dataframe. 

  2. Reshape the dataset into paragraphs from documents and check for balance in your labels. 

  3. Split up a text document corpus into tokens, or individual words and punctuations. Then clean the data by removing specific words and spellings.

  4. Create a Document Feature Matrix, divide it into train and test sets and run a Naive Bayes model. Then examine the model’s prediction accuracy and learn about accuracy, precision, and recall.   

  5. Run Naive Bayes models for a second and third time. Then examine the models' predictions and compare the model outputs with results from the previous task.

Recommended experience

Basic knowledge of the statistical programming language R

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Instructor

Nicole Baerg
Coursera Project Network
10 Courses3,470 learners

Offered by

How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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