Tutorial 1 covers data acquisition from the U.S. GPO API. The code files here implement retrieving one day, four days, and ten years of data.
Tutorial 2 covers latent Dirichlet allocation topic modeling using MALLET. The data here are U.S. Senate Additional Statements. The Python file text_processor.py processes the raw data retrieved in Tutorial 1.
Tutorial 3 covers support vector machine classification. The code files here implement classification using the iris dataset, including visualization.
The extras directory contains the raw data retrieved in Tutorial 1.
Python distributions:
- Continuum Analytics Anaconda: https://www.continuum.io/
- Enthought Canopy: https://www.enthought.com/products/canopy/
Python package repository: https://pypi.python.org/pypi
R Studio: https://www.rstudio.com/
R package repository: https://cran.r-project.org/
MySQL: https://www.mysql.com/
MongoDB: https://www.mongodb.org/
Apache’s Cassandra: http://cassandra.apache.org/
Redis: http://redis.io/
TACIT: http://tacit.usc.edu/
Natural Language Toolkit: http://www.nltk.org/
Genism: https://radimrehurek.com/gensim/
Stanford CoreNLP: http://stanfordnlp.github.io/CoreNLP/
MALLET: http://mallet.cs.umass.edu/topics.php
Matlab Topic Modeling Toolbox: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
scikit-learn: http://scikit-learn.org/stable/
WEKA: http://www.cs.waikato.ac.nz/ml/weka/
UCINET: https://sites.google.com/site/ucinetsoftware/home
Pajek: http://mrvar.fdv.uni-lj.si/pajek/
Siena/RSiena: https://www.stats.ox.ac.uk/~snijders/siena/
Gephi: https://gephi.org/
d3.js: https://d3js.org/
graphviz: http://www.graphviz.org/