Naive Bayes Classifier for Hotel Reviews

A Naive Bayes classifier to identify hotel reviews as either truthful or deceptive, and either positive or negative.

Training Data:

• A top-level directory with two sub-directories, one for positive reviews and another for negative reviews .
• 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).

Two files:

nblearn.py:

The program will learn a naive Bayes model, and write the model parameters to a file called nbmodel.txt

nblclassify.py:

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.