Due: January 27, 2022 (for students who registered late, the assignment is due two weeks after registration).
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.
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:
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.
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.
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.
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.
Development data. While developing your programs, you should reserve some of the data as development data in order to test the performance of your programs. The submission script on Vocareum will use folds 2, 3, and 4 as training data, and fold 1 as development data: that is, it will run 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. In your own development 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.
Problem formulation. You may treat the problem as two binary classification problems (truthful/deceptive and positive/negative), or as a 4-class single classification problem. Choose whichever works better.
Smoothing and unknown tokens. You should implement some method of smoothing for the training data and a way to handle unknown vocabulary in the test data, otherwise your programs won’t work. For example, you can use add-one smoothing on the training data, and simply ignore unknown tokens in the test data. You may use more sophisticated methods which you implement yourselves.
Tokenization. You’d need to develop some reasonable method of identifying tokens in the text (since these are the features for the naive Bayes classifier). Some common options are removing certain punctuation, or lowercasing all the letters. You may also find it useful to ignore certain high-frequency or low-frequency tokens. You may use any tokenization method which you implement yourselves. Experiment, and choose whichever works best.
Location of data. As mentioned above, the training and test data are read from a central directory on Vocareum. Do not make assumptions about the location of the data. For example, you may find that the submission data are located a certain depth from the root, and be tempted to read directory names (for training) using the depth from the root, for example using something like path[8]; but then the grading data could be located at a different depth, and this will break your program. Calculating the depth of a directory from the bottom will solve this problem, for example path[-3].
This is an individual assignment. You may not work in teams or collaborate with other students. You must be the sole author of 100% of the code you turn in.
You may not look for solutions on the web, or use code you find online or anywhere else.
You may not download the data from any source other than the files provided on Blackboard, and you may not attempt to locate the test data on the web or anywhere else.
You may use packages in the Python Standard Library, as well as numpy. You may not use any other packages.
You may use external resources to learn basic functions of Python (such as reading and writing files, handling text strings, and basic math), but the extraction and computation of model parameters, as well as the use of these parameters for classification, must be your own work.
Failure to follow the above rules is considered a violation of academic integrity, and is grounds for failure of the assignment, or in serious cases failure of the course.
We use plagiarism detection software to identify similarities between student assignments, and between student assignments and known solutions on the web. Any attempt to fool plagiarism detection, for example the modification of code to reduce its similarity to the source, will result in an automatic failing grade for the course.
Please discuss any issues you have on the Piazza discussion boards. Do not ask questions about the assignment by email; if we receive questions by email where the response could be helpful for the class, we will ask you to repost the question on the discussion boards.