Description
Overview
Text classification is an important task in natural language processing. It aims to categorise a
given document, paragraph, or a sentence into a set of predefined classes. Text
classification has applications in business, social media, electronic health record, education,
to name just a few. In this assignment, you will conduct text classification by using Naïve
Bayes Classifier on a benchmark dataset.
You are provided with the 20 newsgroups dataset and the code explained in Week 6’s
tutorial. They can be obtained from Moodle by downloading the file “20-newsgroups_textclassification- master.zip.” [1] under “Week 6 Lab.”
What you need to complete this lab:
• The 20 newsgroups dataset and the code (“Multinomial Naive Bayes- BOW with
TF.ipynb”).
• The lecture & tutorial content and recordings in Week 6.
• Python 3 programming environment with required libraries, packages, and modules.
Objectives
• Understand Bayes’ formula and Naïve Bayes Classifier.
• Understand text classification and the pre-processing procedure in natural language
processing.
• Learn to conduct text classification on a benchmark data set.
• Learn to use libraries, packages and modules related to text classification.
Questions (5 marks)
Run the code “Multinomial Naive Bayes- BOW with TF.ipynb” (further modifications in the
code may be required) and answer the following questions:
1. Describe the key steps for data preparation and feature extraction (1 mark).
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2. Report the overall classification results, including precision, recall, and f1-score.
Explain the meaning of these criteria (1 mark).
3. Plot the confusion matrix for your classification result. Find the pair of classes that
confuses the classifier most. Is this result consistent with your expectation? (1 mark).
4. Based on the confusion matrix, report the individual accuracy scores for each class
(1 mark).
5. Train a Complement Naive Bayes classifier and compare its classification results
with those of Multinomial Naive Bayes (1 mark).
Submission
• Submit a single PDF file which contains your answers to the questions of all tasks.
All questions are to be answered. A clear and complete explanation needs to be
provided with each answer.
• You must show your name and student number on the first page of the PDF report.
• Your PDF report should begin with a short introduction to the lab and the dataset.
• Submit the PDF file via the submission link on Moodle.
• The PDF file must contain typed text of your answers (do not submit a scan of a
handwritten document. Any handwritten document will be ignored). The document
can include computer generated graphics and illustrations (hand-drawn graphics
and illustrations will be ignored).
• The PDF document of your answers should be no more than 4 pages including all
graphs and illustrations. Appendix is allowed and will not be counted for the 4-page
limit.
• The size limit for this PDF report is 20MB.
• Late submission will not be accepted without academic consideration being granted.
References
[1] https://github.com/gokriznastic/20-newsgroups_text-classification.
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