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Social Media Mining and Text Data Analysis With Natural Language Processing in R
Introduction to the course
About the course
Data & Scripts
Introduction to R & RStudio
Conclusion to Section 1
Read in Data From Different Sources
Read in CSV & Excel Data
Read in Online CSV Data
Read in Zipped File
Read in databases
Read in JSON Data
Conclusions to Section 2
Webscraping: Extract Data from Webpages
Read in Data From Online Google Sheets
Read in Data from Online HTML Tables-Part 1
Read in Data From HTML Tables-Part 2
Get & Clean Data From HTML Tables
Read Text Data from HTML
Introduction to Selector Gadget
More Web-scraping With rvest
Another Way of Accessing Web Data
Conclusion to Section 3
Start With APIs
What is an API?
Scraping the Guardian Newspaper
Text Data Mining from Social Media
Extract Data from Facebook
Get More out Of Facebook
Set Up a Twitter App
Extract Tweets With R
More Twitter Data Extraction
Geo-locational Information from Twitter
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
Set up the FourSquare App
Extract Reviews for Venues on FourSquare
Conclusion to Section 5
Exploring Text Data For Preliminary Ideas
Explore Tweet Data
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
Count Unique Words in Tweets
Visualizing Text Data as TF-IDF
TF-IDF in Graphical Form
Conclusions
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
Twitter Sentiments: Mugabe
Tidy Sentiments: Sentiment Analysis With tidytext
Examine the Polarity of Text
Examine the Polarity of Tweets
Topic Modelling of a Document
Topic Modelling of Multiple Documents
Topic Modelling of Tweets Using Quanteda
Conclusion to Section 7
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
The Doc2Vec Approach
Doc2Vec Approach For Predicting a Binary Outcome
Doc2Vec Approach for Multi-class Classification
Network Analysis
A Small (Social) Network
Some Theory
Build & Visualize a Network
Network of Emails
More on Network Visualisation
Analysis of Tweet Network
Identify Word Pair Networks
Network of Words
Data & Scripts
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