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Social Media Mining and Text Data Analysis With Natural Language Processing in R
Introduction to the course
About the course (7:58)
Data & Scripts
Introduction to R & RStudio
Conclusion to Section 1 (1:18)
Read in Data From Different Sources
Read in CSV & Excel Data (9:56)
Read in Online CSV Data (4:04)
Read in Zipped File (3:04)
Read in databases (8:23)
Read in JSON Data (5:28)
Conclusions to Section 2
Webscraping: Extract Data from Webpages
Read in Data From Online Google Sheets (4:03)
Read in Data from Online HTML Tables-Part 1 (4:13)
Read in Data From HTML Tables-Part 2 (6:24)
Get & Clean Data From HTML Tables (7:30)
Read Text Data from HTML (8:52)
Introduction to Selector Gadget (6:11)
More Web-scraping With rvest (8:52)
Another Way of Accessing Web Data (2:52)
Conclusion to Section 3 (1:35)
Start With APIs
What is an API?
Scraping the Guardian Newspaper (6:42)
Text Data Mining from Social Media
Extract Data from Facebook (4:12)
Get More out Of Facebook
Set Up a Twitter App (3:52)
Extract Tweets With R (5:21)
More Twitter Data Extraction (6:28)
Geo-locational Information from Twitter (5:06)
Get Location Specific Trends (2:02)
Learn More About the Followers of a Twitter Handle (6:55)
Another Way of Extracting Information From Twitter- the rtweet Package (3:18)
Geolocation Specific Tweets With "rtweet" (7:49)
More Data Extraction Using rtweet (3:18)
Locations of Tweets (4:02)
Mining Github Using R (7:04)
Set up the FourSquare App (4:32)
Extract Reviews for Venues on FourSquare (11:28)
Conclusion to Section 5 (1:46)
Exploring Text Data For Preliminary Ideas
Explore Tweet Data (7:51)
A Brief Explanation (4:22)
EDA With Text Data (9:02)
Examine Multiple Document Corpus of Text (5:30)
Brief Introduction to tidytext (8:28)
Text Exploration & Visualization with tidytext (11:09)
Explore Multiple Texts with tidytext (9:22)
Count Unique Words in Tweets (4:54)
Visualizing Text Data as TF-IDF (7:55)
TF-IDF in Graphical Form (5:49)
Conclusions (1:18)
Natural Language Processing: Sentiment Analysis
Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy (12:29)
Wordclouds for Visualizing Reviews (10:32)
Tidy Wordclouds (5:35)
Quanteda Wordcloud (8:34)
Word Frequency in Text Data (3:24)
Twitter Sentiments: Mugabe (4:52)
Tidy Sentiments: Sentiment Analysis With tidytext (8:38)
Examine the Polarity of Text (10:58)
Examine the Polarity of Tweets (6:24)
Topic Modelling of a Document (8:15)
Topic Modelling of Multiple Documents (14:19)
Topic Modelling of Tweets Using Quanteda (8:21)
Conclusion to Section 7 (2:14)
Text Data and Machine learning
Clustering for Text Data (7:17)
Clustering Tweets with Quanteda (4:35)
Regression on Text Data (6:11)
Identify Spam Emails with Supervised Classification (10:09)
Introduction to RTextTools (6:16)
The Doc2Vec Approach (4:00)
Doc2Vec Approach For Predicting a Binary Outcome (12:24)
Doc2Vec Approach for Multi-class Classification (9:00)
Network Analysis
A Small (Social) Network (2:43)
Some Theory (4:25)
Build & Visualize a Network (14:31)
Network of Emails (6:50)
More on Network Visualisation (4:10)
Analysis of Tweet Network (8:13)
Identify Word Pair Networks (9:13)
Network of Words (4:42)
Doc2Vec Approach For Predicting a Binary Outcome
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