This assignment counts for 10% of the course grade.
Assignments turned in after the deadline but before October 1 are subject to a 20% grade penalty.
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.
A set of training and development data is available as a compressed ZIP archive on Blackboard. The uncompressed archive contains the following files:
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.
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:
nbclassify.py to successfully label new data.
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.
All submissions will be completed through Vocareum; please consult the instructions for how to use 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.
After the due date, we will train your model on the full training data (including development data), run your classifier on unseen test data, and compute the F1 score of your output compared to a reference annotation for each of the four classes (truthful, deceptive, positive, and negative). Your grade will be based on the performance of your classifier. We will calculate the mean of the four F1 scores and scale it to the performance of a naive Bayes classifier developed by the instructional staff (so if that classifier has F1=0.8, then a score of 0.8 will receive a full credit, and a score of 0.72 will receive 90% credit).
Note that the measure for grading is the macro-average over classes; macro- and micro-averaging are explained in Manning, Raghavan and Schutze, Introduction to information retrieval, Chapter 13: Text classification and Naive Bayes. For more information on F1, see Manning, Raghavan and Schutze, Introduction to information retrieval, Chapter 8: Evaluation in information retrieval.
nblearn.py on a directory containing only folds 2, 3,
and 4, and it will run nbclassify.py on a directory
with a modified version of fold 1, where directory and file names
are masked. While developing on your own you may use
different splits of the data (but to get the same results
as the submission script, you'll need to use the same split).
The grading script will use all 4
folds for training, and unseen data for testing.