Text Mining and Natural Language Processing in R
- Offered byEduonix
Text Mining and Natural Language Processing in R at Eduonix Overview
Duration | 15 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Credential | Certificate |
Text Mining and Natural Language Processing in R at Eduonix Highlights
- Earn a Certificate on successful course completion from The University of Washington
- Lifetime Access. No Limits!
- An exclusive hands-on text mining and Natural Language Processing (NLP) training for data science applications in R
- Learn online from any location
Text Mining and Natural Language Processing in R at Eduonix Course details
- Data mining and analysis professionals
- software developers and programmers
- statisticians
- computational and educational professionals
- Learn the fundamentals of R programming language and different forms of data visualization
- Know how to use easily use packages like caret, dplyr to work with real data in R
- Learn how to manipulate data and generate reports using R
- Projects in R: Learn R Creating Data Science Projects, is an online course by Eduonix Learning Solutions that will help you to implement the methods using real data obtained from different sources. The participants will also learn to use the common social media mining and natural language processing packages to extract insights from text data.
Text Mining and Natural Language Processing in R at Eduonix Curriculum
Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
About the Course and Instructor Preview
Data and Scripts For the Course
Introduction to R and RStudio
Conclusion to Section 1
Section 2 : Reading in Data from Different Sources
Read in CSV & Excel Data Preview
Read in Data from Online CSV
Read in Zipped File
Read Data from a Database
Read in JSON Data
Read in Data from PDF Documents
Read in Tables from PDF Documents
Conclusion to Section 2
Section 3 : Webscraping: Extract Data from Webpages
Read in Data From Online Google Sheets Preview
Read in Data from Online HTML Tables-Part 1
Read in Data from Online HTML Tables-Part 2
Get and Clean Data from HTML Tables
Read Text Data from an HTML Page
Introduction to Selector Gadget
More Webscraping With rvest-IMDB Webpage
Another Way of Accessing Webpage Elements
Conclusions to Section 3
Section 4 : Introduction to APIs
What is an API? Preview
Extract Text Data from Guardian Newspaper
Section 5 : Text Data Mining from Social Media
Extract Data from Facebook
Get More out Of Facebook
Set up a Twitter App for Mining Data from Twitter
Extract Tweets Using R Preview
More Twitter Data Extraction Using R
Get Tweet Locations
Get Location Specific Trends
Learn More About the Followers of a Twitter Handle
Another Way of Extracting Information From Twitter- the rtweet Package
Geolocation Specific Tweets With "rtweet"
More Data Extraction Using rtweet
Locations of Tweets
Mining Github Using R Preview
Set up the FourSquare App
Extract Reviews for Venues on FourSquare
Conclusions to Section 5
Section 6 : Exploring Text Data For Preliminary Ideas
Explore Tweet Data Preview
A Brief Explanation
EDA With Text Data
Examine Multiple Document Corpus of Text
Brief Introduction to tidytext
Text Exploration & Visualization with tidytext
Explore Multiple Texts with tidytext Preview
Count Unique Words in Tweets
Visualizing Text Data as TF-IDF
TF-IDF in Graphical Form
Conclusions to Section 6
Section 7 : Natural Language Processing: Sentiment Analysis
Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy
Wordclouds for Visualizing Reviews
Tidy Wordclouds
Quanteda Wordcloud
Word Frequency in Text Data
Tweet Sentiments- Mugabe's Ouster
Tidy Sentiments- Sentiment Analysis Using tidytext
Examine the Polarity of Text
Examine the Polarity of Tweets
Topic Modelling a Document
Topic Modelling Multiple Documents
Topic Modelling Tweets Using Quanteda
Conclusions to Section 7
Section 8 : Text Data and Machine Learning
Clustering for Text Data
Clustering Tweets with Quanteda
Regression on Text Data
Identify Spam Emails with Supervised Classification
Introduction to RTextTools
More on RTextTools
The Doc2Vec Approach
Doc2Vec Approach For Predicting a Binary Outcome
Doc2Vec Approach for Multi-class Classification
Section 9 : Network Analysis
A Small (Social) Network
A More Theoretical Explanation
Build & Visualize a Network
Network of Emails
More on Network Visualization
Analysis of Tweet Network
Identify Word Pair Networks
Network of Words
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