Predicting Document Interest on Reddit

Identifying Interesting Documents for Reddit using Recommender Techniques

A Reddit agent that will automatically search the web for interesting text content to share to a subreddit it has been trained on. The agent will make use of a number of machine learning and natural language processing techniques to achieve this goal.

Organistion

The source code is organised into folders as follows:

src: source code for main code files related to the Recommender system.
tools: source code for tools outside the scope of the Recommender system but used to download and extract post data from the Reddit API.
figures: source code related to the generation of figures for this thesis.

Requirements

The code is written for the standard CPython 2.7 interpeter. This is due to certain library requirements.

The following libraries are required (in pip format):

nltk==2.0.4
matplotlib==1.3.1
numpy==1.8.1
nose==1.3.3
MySQL-python==1.2.5
goose-extractor==1.0.20
beautifulsoup4==4.3.2
lxml==3.3.5
argparse==1.2.1
scikit-learn==0.14.1
scipy==0.14.0
requests==2.3.0

These should ideally be installed in a seperate virtualenv to prevent any previously installed libraries from influencing the runtime behavior.

Once a virtualenv has been setup and activated, you can install the above libraries using the following command:

pip install -r requirements.txt

Note that the open source tool WikiExtractor.py is also required but is already included amongst the source code for convenience.

Noteworthy Files

The following files are executable python scripts. In most cases executing the script without any input parameters will display the expected input patterns as a help.

tools/post_extractor.py - tool to download subreddit data with.
tools/ds_tool.py - maintenance tool for working with previsouly downloaded datasets.
figures/evaluate.py - evaluate the performance of Bag of Words and Bag of Concepts according to input parameters and plot the results in ROC and PR curves.
src/main.py - test run the Bag of Words recommender system.
src/nlpmain.py - test run the Bag of Concepts system.
src/wiki_setup.py - setup a Wikipedia Database Index from some input data dump.

Running Bag of Concepts

In order to run any code related to Bag of Concepts you first need to index a Wikipedia article corpus. This can be downloaded from Wikipedia in bz2 format here.

You should download the pages-articles.xml.bz2 file (approx 11GB as of August 2014)

Once downloaded you need to setup a MySQL database. Assuming you create a database named "WikiIndex" then create a file in the src directory named db.json with the following contents:

{
    'host': 'localhost',
    'user': 'root',
    'passwd': 'passwordhere',
    'db': 'WikiIndex'
}

Change the above parameters according to your database setup.

Once the Wikipedia dump has been downloaded and the relevant database setup you need to run the wiki setup script to create the database index. Typically this would be done as follows:

python wiki_setup.py pages-articles.xml.bz2 --engine MYISAM

This process takes a very long time (up to 28 hours on my SSD) due to the parsing of 16 million articles of text and saving them to disk as a database.

Once the process is completed then all Bag of Concepts methods should work as expected.

Running Tests

Numerous tests are included amongst the source code to ensure that functionality is running as expected after setup. These tests can be activated using nose in the root directory:

nosetests

MichaelAquilina / Reddit-Recommender-Bot