Coding Assignment 1: Naive Bayes

This assignment counts for 10% of the course grade.

Assignments turned in after the deadline but before January 30 are subject to a 20% grade penalty.

Overview
In this assignment you will write a naive Bayes classifier to identify hotel reviews as either truthful or deceptive, and either positive or negative. You will be using the word tokens as features for classification. The assignment will be graded based on the performance of your classifiers, that is how well they perform on unseen test data compared to the performance of a reference classifier.

Data
A set of training and development data is available as a compressed ZIP archive on Blackboard. The uncompressed archive contains the following files:

A top-level directory with two sub-directories, one for positive reviews and another for negative reviews (plus license and readme files which you won’t need for the exercise).

Each of the subdirectories contains two sub-directories, one with truthful reviews and one with deceptive reviews.

Each of these subdirectories contains four subdirectories, called “folds”.

Each of the folds contains 80 text files with English text (one review per file).

The submission script will train your model on part of the training data, and report results on the remainder of the training data (reserved as development data; see below). The grading script will train your model on all of the training data, and test the model on unseen data in a similar format. The directory structure and file names of the test data will be masked so that they do not reveal the labels of the individual test files.

Programs
You will write two programs in Python 3 (Python 2 has been deprecated): nblearn.py will learn a naive Bayes model from the training data, and nbclassify.py will use the model to classify new data.

The learning program will be invoked in the following way:

> python nblearn.py /path/to/input

The argument is the directory of the training data; the program will learn a naive Bayes model, and write the model parameters to a file called nbmodel.txt. The format of the model is up to you, but it should follow the following guidelines:

The model file should contain sufficient information for nbclassify.py to successfully label new data.

The model file should be human-readable, so that model parameters can be easily understood by visual inspection of the file.

The classification program will be invoked in the following way:

> python nbclassify.py /path/to/input

The argument is the directory of the test data; the program will read the parameters of a naive Bayes model from the file nbmodel.txt, classify each file in the test data, and write the results to a text file called nboutput.txt in the following format:

label_a label_b path1

label_a label_b path2 

⋮

In the above format, label_a is either “truthful” or “deceptive”, label_b is either “positive” or “negative”, and pathn is the path of the text file being classified.

Note that in the training data, it is trivial to infer the labels from the directory names in the path. However, directory names in the development and test data on Vocareum will be masked, so the labels cannot be inferred this way.

Submission
All submissions will be completed through Vocareum.

Multiple submissions are allowed; only the final submission will be graded. Each time you submit, a submission script is invoked. The submission script uses a specific portion of the training data as development data; it trains your model on the remaining training data, runs your classifier on the development data, and reports the results. Do not include the data in your submission: the submission script reads the data from a central directory, not from your personal directory. You should only upload your program files to Vocareum, that is nbclassify.py and nblearn.py (plus any required auxiliary files, such as code shared between the programs or a word list that you wrote yourself).

You are encouraged to submit early and often in order to iron out any problems, especially issues with the format of the final output.

The performance of you classifier will be measured automatically; failure to format your output correctly may result in very low scores, which will not be changed.

For full credit, make sure to submit your assignment well before the deadline. The time of submission recorded by the system is the time used for determining late penalties. If your submission is received late, whatever the reason (including equipment failure and network latencies or outages), it will incur a late penalty.

If you have any issues with Vocareum with regards to logging in, submission, code not executing properly, etc., please make a post on Piazza so the instructional team can look into the issue.