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COMP9414: Artificial Intelligence
Assignment 2: Sentiment Analysis
Due Date: Week 9, Friday, July 31, 11:59 p.m.
Value: 25%
This assignment is inspired by a typical real-life scenario. Imagine you have been hired as a
Data Scientist by a major airline company. Your job is to analyse the Twitter feed to determine
customer sentiment towards your company and its competitors.
In this assignment, you will be given a collection of tweets about US airlines. The tweets have been
manually labelled for sentiment. Sentiment is categorized as either positive, negative or neutral.
Important: Do not distribute these tweets on the Internet, as this breaches Twitter’s
Terms of Service.
You are expected to assess various supervised machine learning methods using a variety of features
and settings to determine what methods work best for sentiment classification in this domain. The
assignment has two components: programming to produce a collection of models for sentiment
analysis, and a report to evaluate the effectiveness of the models. The programming part involves
development of Python code for data preprocessing of tweets and experimentation of methods using
NLP and machine learning toolkits. The report involves evaluating and comparing the models
using various metrics, and comparison of the machine learning models to a baseline method.
You will use the NLTK toolkit for basic language preprocessing, and scikit-learn for feature construction
and evaluating the machine learning models. You will be given an example of how to
use NLTK and scikit-learn for this assignment (example.py). For the sentiment analysis baseline,
NLTK includes a hand-crafted (crowdsourced) sentiment analyser, VADER,1 which may perform
well in this domain because of the way it uses emojis and other features of social media text to
intensify sentiment, however the accuracy of VADER is difficult to anticipate because: (i) crowdsourcing
is in general highly unreliable, and (ii) this dataset might not include much use of emojis
and other markers of sentiment.
Data and Methods
A training dataset is a tsv (tab separated values) file containing a number of tweets, with one
tweet per line, and linebreaks within tweets removed. Each line of the tsv file has three fields:
instance number, tweet text and sentiment (positive, negative or neutral). A test dataset is a tsv
file in the same format as the training dataset except that your code should ignore the sentiment
field. Training and test datasets can be drawn from a supplied file dataset.tsv (see below).
For all models except VADER, consider a tweet to be a collection of words, where a word is a string
of at least two letters, numbers or the symbols #, @, , $ or %, delimited by a space, after removing
all other characters (two characters is the default minimum word length for CountVectorizer in
scikit-learn). URLs should be treated as a space, so delimit words. Note that deleting “junk”
characters may create longer words that were previously separated by those characters.
Use the supervised learning methods discussed in the lectures: Decision Trees (DT), Bernoulli
Naive Bayes (BNB) and Multinomial Naive Bayes (MNB). Do not code these methods: instead use
1https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109
the implementations from scikit-learn. Read the scikit-learn documentation on Decision Trees2 and
Naive Bayes,3 and the linked pages describing the parameters of the methods. Look at example.py
to see how to use CountVectorizer and train and test the machine learning algorithms, including
how to generate metrics for the models developed.
The programming part of the assignment is to produce DT, BNB and MNB models and your own
model for sentiment analysis in Python programs that can be called from the command line to train
and classify tweets read from correctly formatted tsv files. The report part of the assignment is to
analyse these models using a variety of parameters, preprocessing tools, scenarios and baselines.
Programming
You will produce and submit four Python programs: (i) DT sentiment.py (ii) BNB sentiment.py,
(iii) MNB sentiment.py and (iv) sentiment.py. The first three of these are standard models as
defined below. The last is a model that you develop following experimentation with the data. Use
the given dataset (dataset.tsv) containing 5000 labelled tweets to develop the models.
These programs, when called from the command line with two file names as arguments, the
first a training dataset and the second a test dataset, should print (to standard output), the
instance number and sentiment produced by the classifier of each tweet in the test set when
trained on the training set (one per line with a space between them) – each sentiment being the
string “positive”, “negative” or “neutral”. For example:
python3 DT sentiment.py training.tsv test.tsv > output.txt
should write to the file output.txt the instance number and sentiment of each tweet in test.tsv,
as determined by the Decision Tree classifier trained on training.tsv.
When reading in training and test datasets, make sure your code reads all the instances (some
Python readers use “excel” format, which uses double quotes as separators).
Standard Models
Train the three standard models on the supplied dataset of 5000 tweets (the whole of dataset.tsv).
For Decision Trees, use scikit-learn’s Decision Tree method with criterion set to ’entropy’ and with
random state=0. Scikit-learn’s Decision Tree method does not implement pruning, rather you
should make sure Decision Tree construction stops when a node covers fewer than 50 examples
(1% of the training set). Decision Trees are likely to lead to fragmentation, so to avoid overfitting
and reduce computation time, for all Decision Tree models use as features only the 1000 most
frequent words from the vocabulary (after preprocessing to remove “junk” characters as described
above). Write code to train and test a Decision Tree model in DT sentiment.py.
For both BNB and MNB, use scikit-learn’s implementations, but use all of the words in the
vocabulary as features. Write two Pythons programs for training and testing Naive Bayes models,
one a BNB model and one an MNB model, in BNB sentiment.py and MNB sentiment.py.
Your Model
Develop your best model for sentiment classification by varying the number and type of input
features for the learners, the parameters of the learners, and the training/test set split, as described
in your report (see below). Submit one program, sentiment.py, that trains and tests a model.
2https://scikit-learn.org/stable/modules/tree.html
3https://scikit-learn.org/stable/modules/naive bayes.html
Report
In the report, you will first evaluate the standard models, then present your own model. For
evaluating all models, report the results of training on the first 4000 tweets in dataset.tsv (the
“training set”) and testing on the remaining 1000 tweets (the “test set”), rather than using the
full dataset of 5000 tweets for training, so stopping the Decision Tree classifiers when nodes cover
less than 40 tweets rather than 50. Use the metrics (micro- and macro-accuracy, precision, recall
and F1) and classification reports from scikit-learn. Show the results in either tables or plots, and
write a short paragraph in your response to each item below. The answer to each question should
be self contained. Your report should be at most 10 pages. Do not include appendices.
1. (1 mark) Give simple descriptive statistics showing the frequency distribution for the sentiment
classes for the whole dataset of 5000 tweets. What do you notice about the distribution?
2. (2 marks) Develop BNB and MNB models from the training set using (a) the whole vocabulary,
and (b) the most frequent 1000 words from the vocabulary (as defined using CountVectorizer, after
preprocessing by removing “junk” characters). Show all metrics on the test set comparing the two
approaches for each method. Explain any similarities and differences in results.
3. (2 marks) Evaluate the three standard models with respect to the VADER baseline. Show all
metrics on the test set and comment on the performance of the baseline and of the models relative
to the baseline.
4. (2 marks) Evaluate the effect of preprocessing the input features by applying NLTK English
stop word removal then NLTK Porter stemming on classifier performance for the three standard
models. Show all metrics with and without preprocessing on the test set and explain the results.
5. (2 marks) Evaluate the effect that converting all letters to lower case has on classifier performance
for the three standard models. Show all metrics with and without conversion to lower case
on the test set and explain the results.
6. (6 marks) Describe your best method for sentiment analysis and justify your decision. Give
some experimental results for your method trained on the training set of 4000 tweets and tested
on the test set of 1000 tweets. Provide a brief comparison of your model to the standard models
and the baseline (use the results from the previous questions).
Submission
• Submit all your files using a command such as (this includes Python code and report):
give cs9414 ass2 DT*.py BNB*.py MNB*.py sentiment.py report.pdf
• Your submission should include:
– Your .py files for the specified models and your model, plus any .py “helper” files
– A .pdf file containing your report
• When your files are submitted, a test will be done to ensure that one of your Python files
runs on the CSE machine (take note of any error messages printed out)
• When running your code on CSE machines:
– Set SKLEARN SITE JOBLIB=TRUE to avoid warning messages
– Do not download NLTK in your program: CSE machines have NLTK installed
• Check that your submission has been received using the command:
9414 classrun -check ass2
Assessment
Marks for this assignment are allocated as follows:
• Programming (auto-marked): 10 marks
• Report: 15 marks
Late penalty: 5 marks per day or part-day late off the mark obtainable for up to 3
(calendar) days after the due date
Assessment Criteria
• Correctness: Assessed on standard input tests, using calls such as:
python3 DT sentiment.py training.tsv test.tsv > output.txt
Each such test will give two files, a training dataset and a test dataset, which contain any
number of tweets (one on each line) in the correct format. The training and test datasets
can have any names, not just training.tsv and test.tsv, so read the file names from
sys.argv. The output should be a sequence of lines (one line for each tweet) giving the
instance number and classified sentiment, separated by a space and with no extra spaces or
lines. There are 2 marks allocated for correctness of each of the three standard models.
For your own method, 4 marks are allocated for correctness of your methods on test sets of
tweets that include unseen examples.
• Report: Assessed on correctness and thoroughness of experimental analysis, and clarity and
succinctness of explanations.
There are 9 marks allocated to items 1–5 as above, and 6 marks for item 6. Of these 6 marks,
2 marks are for the explanation of your choice of model, 2 marks are for the experimental
analysis of your model, and 2 marks are for the evaluation of your model in comparison to
the standard models and baseline.
Plagiarism
Remember that ALL work submitted for this assignment must be your own work and no code
sharing or copying is allowed. You may use code from the Internet only with suitable attribution
of the source in your program. Do not use public code repositories. All submitted assignments will
be run through plagiarism detection software to detect similarities to other submissions, including
from past years. You should carefully read the UNSW policy on academic integrity and plagiarism
(linked from the course web page), noting, in particular, that collusion (working together on an
assignment, or sharing parts of assignment solutions) is a form of plagiarism. There is also a new
plagiarism policy starting this term with more severe penalties.

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