Description
What to Submit
When you have completed the assignment, move or copy your python scripts in a directory (e.g.,
assignment1), and use the following command to electronically submit your files within that directory:
% submit 4415 a1 dstats.py songprofiling.py artistprofiling.py songsim.py artistsim.py team.txt
The team.txt file includes information about the team members (first name, last name, student ID,
login, yorku email). You can also submit the files individually after you complete each part of the
assignment– simply execute the submit command and give the filename that you wish to submit. Make
sure you name your files exactly as stated (including lower/upper case letters). Failure to do so will
result in a mark of 0 being assigned. You may check the status of your submission using the command:
% submit -l 4415 a1
1 https://www.lyricsfreak.com/
A. Descriptive Statistics (24%, 4% each)
Write a python program (dstats.py) that given a collection of English song lyrics computes and
prints out (in the STDOUT) the following statistics:
number of artists/bands in the collection (numOfArtists)
number of songs in the collection (numOfSongs)
average number of songs per artist/band (avgNumOfSongs)
average number of unique words per song in the collection (avgNumOfWords)
average number of unique words per song of an artist/band, sorted by artist/band name in an
alphanumerically ascending order, i.e., a->z (pairsOfArtistAvgNumOfWords)
plot a bar chart that shows the top-10 pairs found in the previous bullet, where the x-axis
represents the artists/band and the y-axis represents the average number of words. Note that
the top-1 artist/band is the one with the largest average number of unique words per song.
The collection of songs is provided in a file songdata.csv that follows the same format as the
original data set provided by Kaggle. The contents of the file might vary when testing your code.
Running the script:
Your script should be run as follows:
% python3 dstats.py < songdata.csv
Hints:
In order to perform a more meaningful text analysis, you need to use a word tokenizer to tokenize the
song lyrics into individual words. You also need to eliminate stopwords — the very common words in
English — and words just one character long, as they are not interesting for the analysis. The Natural
Language Toolkit2
library (nltk) provides this functionality off the shelf.
Use the matplotlib.pyplot module to create the plot. You can follow the example at pythonspot3
about using matplotlib to create a bar chart: https://pythonspot.com/en/matplotlib-bar-chart/
2 https://www.nltk.org/
3 https://pythonspot.com
B. Creating Profiles of Songs and Artists (50%, 25% each)
Write two python programs with the following descriptions:
songprofiling.py: Given a collection of English songs, it computes and prints out (in the
STDOUT) the profile of each song in the collection; the profile of a song consists of the top-50
more important words of its lyrics, based on the tf-idf score. Before printing out, you need to
sort the pairs of (word, score) in descending order of score.
artistprofiling.py: Given a collection of English songs, it computes and prints out (in
the STDOUT) the profile of each artist/band in the collection; the profile of an artist/band
consists of the top-100 more important words of their song lyrics, based on the tf-idf score.
Before printing out, you need to sort the pairs of (word, score) in descending order of score.
The collection of songs is provided in a file songdata.csv that follows the same format as the
original data set provided by Kaggle. The contents of the file might vary when testing your code.
Running the script:
Your scripts should be run as follows:
% python3 songprofiling.py < songdata.csv
% python3 artistprofiling.py < songdata.csv
Hints:
The scikit-learn has a built-in tf-idf implementation that you can use off the shelf. You are also
welcome to build your own tf-idf implementation, based on the tutorial material presented in class (let
us know if you have followed this path!).
