Description
Problem statement:
Detecting popular and trending topics from the news articles is an important task for
public opinion monitoring. In this project, your task is to perform text data analysis
over a dataset of Australian news from ABC (Australian Broadcasting Corporation)
using both RDD and DataFrame APIs of Spark with Python. The problem is to
compute the weights of each term regarding each year in the news articles dataset and
then select the top-k most important terms in each year.
Input files:
The dataset you are going to use contains data of news headlines published over
several years. In this text file, each line is a headline of a news article, in format of
“date,term1 term2 … … “. The date and texts are separated by a comma, and the terms
are separated by the space character.
A sample file is like below:
20030219,council chief executive fails to secure position
20030219,council welcomes ambulance levy decision
20030219,council welcomes insurance breakthrough
20030219,fed opp to re introduce national insurance
20040501,cowboys survive eels comeback
20040501,cowboys withstand eels fightback
20040502,castro vows cuban socialism to survive bush
20200401,coronanomics things learnt about how coronavirus economy
20200401,coronavirus at home test kits selling in the chinese community
20200401,coronavirus campbell remess streams bear making classes
20201015,coronavirus pacific economy foriegn aid china
20201016,china builds pig apartment blocks to guard against swine flu
This small sample file can be downloaded at:
Term weights computation:
You need to ignore the stop words such as “to”, “the”, and “in”. There is also a stop
word list stored in the file:
To compute the weight for a term regarding a year, please use the TF/IDF model.
Specifically, the TF and IDF can be computed as:
TF(term t, year y) = the number of headlines containing t in y
IDF(term t, dataset D) = log10 (the number of years in D/the number of years having t)
Finally, the term weight of term t regarding the year y is computed as:
Weight(term t, year y, dataset D) = TF(term t, year y)* IDF(term t, dataset D)
Please import math and use math.log10() to compute the term weights, and
round the results to 6 decimal places.
Output format:
If there are N years in the dataset, you should output exactly N lines in your final
output file, and these lines are sorted by years in ascending order. In each line, you
need to output a list of k pairs in format of <term, weight>, and these pairs are sorted
by term weights in descending order. If two terms have the same weight, sort them
alphabetically. Specifically, the format of each line is like: “year\t
Term1,Weight1;Term 2,Weight2;… …;Termk,Weightk”. For example, given the above
data set and k=3, the output should be:
2003 council,1.431364;insurance,0.954243;welcomes,0.954243
2004 cowboys,0.954243;eels,0.954243;survive,0.954243
2020 coronavirus,1.908485;china,0.954243;economy,0.954243
Code format:
Please name your two python files as “project2_rdd.py” and “project2_df.py” for
using RDD and DataFrame APIs, respectively. Compress it in a package named
“zID_proj2.zip” (e.g. z5123456_proj2.zip).
Command of running your code:
We will use the following command to run your code:
$ spark-submit project2_rdd.py input output stopwords k
In this command, input is the input file, output is the output folder, stopwords is the
stop words file, and k is the number of terms returned for each year.
Please ensure that the code you submit can be compiled. Any solution that has
compilation errors will receive no more than 5 points.
Marking Criteria:
Your source code will be inspected and marked based on readability and ease of
understanding. Each solution has 8 marks. Below is an indicative marking scheme:
Result correctness: 6
Efficiency and memory usage: 1
Code structure, Readability, and
Documentation: 1
Submission:
You can submit through Moodle:
If you submit your assignment more than once, the last submission will replace the
previous one. To prove successful submission, please take a screenshot as assignment
submission instructions show and keep it by yourself. If you have any problems in
submissions, please email to siqing.li@unsw.edu.au.
Late submission penalty
5% reduction of your marks for up to 5 days
