Text mining with R
4.0 (12 ratings)
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Text mining with R

Analyze twitter text using R
4.0 (12 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,015 students enrolled
Created by Nisha Kiran
Last updated 4/2016
English
English [Auto-generated]
Current price: $10 Original price: $20 Discount: 50% off
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Includes:
  • 32 mins on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Analyze twitter data by extracting text from Twitter
View Curriculum
Requirements
  • You should already be familiar with R programming language.
Description

Have you always wanted to mine twitter data? Then this course is for you. This course presents example of text mining with R. Twitter text of @pycon and @udemy is used as the data to analyze. It starts by extracting text from Twitter. The extracted text is then transformed to a corpus and then a document-term matrix. After that, frequent words and associations are found from the matrix. A word cloud is used to present important words in documents.

There are three important packages used in the examples: twitteR, tm and wordcloud. Package twitteR provides access to Twitter data, tm provides functions for text mining, and wordcloud visualizes the result with a word cloud.

This course is meant for people who have basic knowledge of R and are interested in learning about text mining, in particular about how to mine data from Twitter. At the end of this course, you will be able to build term-document matrix and word clouds for any user on Twitter. 

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Who is the target audience?
  • This course is meant for students who want to learn about text mining using R. In this course, we will use twitter text to demonstrate text mining.
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Curriculum For This Course
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Mining twitter data
9 Lectures 18:42





Stemming words
02:40

Building a term-document matrix
01:30

Frequent terms and associations
01:22

Word cloud
02:12

Test your knowledge
4 questions
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Another example
6 Lectures 13:22

Transform text
02:33

Stem Words
01:30

Term document matrix
02:44

Frequent terms and associations
02:32

Word cloud
02:41
About the Instructor
Nisha Kiran
4.4 Average rating
86 Reviews
2,966 Students
6 Courses
Instructor

Nisha has been teaching since her grad school years as a Masters student in Computer Science where she worked as a teaching assistant for numerous courses in programming. Currently, she works in the Elearning industry and also helps students with programming problems. Nisha has worked as a software developer for various firms prior to teaching and understands how important it is to have a good grasp over programming fundamentals.

During her grad school, she has gained experience in teaching and how to effectively communicate a concept to someone new to programming. Nisha has worked with numerous students ranging from beginner to advanced and understands the needs of both kinds of audience.