Text mining with R

Analyze twitter text using R
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  • Lectures 15
  • Length 32 mins
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 4/2016 English

Course 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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What are the requirements?

  • You should already be familiar with R programming language.

What am I going to get from this course?

  • Analyze twitter data by extracting text from Twitter

What 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Mining twitter data
Introduction
Preview
00:49
twitterR package
Preview
03:14
Exploring twitteR
Preview
03:05
Retrieving text from Twitter
Preview
00:59
Transforming text
Preview
02:51
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
Section 2: Another example
Retrieve text
Preview
01:22
Transform text
02:33
Stem Words
01:30
Term document matrix
02:44
Frequent terms and associations
02:32
Word cloud
02:41

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Instructor Biography

Nisha Kiran, 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.

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