Lectures
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Day 1: Text Mining (Getting data)
Websites
Webscraping
Webservices
Data serialization
Python object
XML
JSON
YAML
Questions
Reading and writing JSON
Word Vectors
Bag of words model
Term-document matrix
Exercise: US Senate tweets
Links
Webservices
Serialization
Day 2: Text Mining (Functions)
Day 3: Text Mining (NLP)
0. More on working with Twitter
1. Importing example data
2. Word useage: contexts
concordance: how are words used?
similar: what words are used in a similar context?
common_contexts: for two words used in similar contexts, see the contexts
collocations: see words often used together
3. Word useage: frequencies
4. Identifying “important” words
5. Working with raw text data
Manually searching for the content
Tokenization
Creating an nltk.Text object
6. Vector representations
Day 5: Graphics (ggplot2)
Day 6: Twitter slides
Announcements
Slide feedback
Day 7: Network Analysis
Announcements
Slide feedback
Network Analysis
Examples
Graphs
Social Network Analysis
Day 8: Poster Prep
Announcements
Poster notes
Team work
Topics
cron
nlp
Capstone
Spring 2016
Navigation
Lectures
Day 1: Text Mining (Getting data)
Day 2: Text Mining (Functions)
Day 3: Text Mining (NLP)
Day 5: Graphics (ggplot2)
Day 6: Twitter slides
Day 7: Network Analysis
Day 8: Poster Prep
Bootcamps
Appendices
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