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COMP9313 Python
创建日期：2024-05-14 14:31:52
标签： COMP9313
Python

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COMP9313 22T3 Project 2 (16 marks)

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 , 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
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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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