Basic framework for dumping posts from given course
Fixed bug in dump_course_contents.py

Wasn't opening files.
Create infra for forum query, classification

Create interfaces that allow for convenient retrieval of
data from forums. Create interface for classifying posts
by topic from a course.

TODO: Replace dummy classification implementation with
real implementation
Removed corenlp-python from git repo
Implement sentiment analysis (corenlp)

Substantiate the implementation of sentiment analysis using
Stanford's corenlp tools.

TODO: Increase performance -- currently, the sentiment classifier
is extremely slow. This is because PostClassifier launches the
java program for each post (instead of just once for all the posts).
The overhead of starting the program is on the order of two seconds,
and there are over 10,000 posts.

TODO: Make a better sentiment classifier. Either a) train the
Stanford model or b) build one using nltk classifiers.
Add functionality to forum interface, emoticon retrieval

Forum interface now does the heavylifting for choosing params
to connect to database, abstracting away all that from client
apps.

Forum's post_contents optionally takes a MySQL query now.

New tool traverses through each course's posts and dumps
the ones that contain emoticons
Initial commit
Initial commit
Restructure Repository

Restructure the directory organization; add the emoticon.py tool
that retrieves training data for sentiment. Add sentiment training
data files. (NB: training data not yet formatted / cleaned.)
Format training data

Add structure to training data. In particular, make each
sentiment training file a series of pickled objects, where
each object is a tuple containing a list of (unfiltered) words
and corresponding sentiment. There is a bijection between sentiments
and the corresponding emoticon type.
Remove TODO comment
Clean up emoticon.py arg passing
Make forum interface more general

Make the interface for creating a forum object and
connecting to a database more genera (i.e., not bound to
a specific user).
Crude nltk sentiment classifier in place

Adds a crude sentiment analysis tool and functionality to use it;
tool needs heavy refinement and cleanup.
Delete dangling decorator
Create util file

Adds util.py, a module to hold convenient utility functions related
to the task of classification.
Documentation for sentiment classification

Adds some documentation describing key sentiment methods in classify.py.
Build representative samples for train, dev, test

emoticon.py now also constructs randomized samples of training,
dev, and test datasets, using a 60/20/20 split among them.

Cursory tests show that the classifier has a large false
positive rate.
Merge branch 'sentiment_aka'
Create sentiment subdirectory