程序代写代做代考 Java data science Excel data structure data mining html Hive javascript CITS1401 Computational Thinking with Python Project 2 Semester 2 2020

CITS1401 Computational Thinking with Python Project 2 Semester 2 2020
Project 2: How Good (Positive and Patriotic) is Australia? Submission deadline: 5:00 pm, Friday 23rd October 2020
Value: 20% of CITS1401
To be completed individually.
You should construct a Python 3 program containing your solution to the following problem and submit your program electronically on Moodle. No other method of submission is allowed. Your program will be automatically tested on Moodle. Remember your first two checks against the tester on Moodle will not have any penalty. However any further check will carry 10% penalty per check.
You are expected to have read and understood the University’s guidelines on academic conduct. In accordance with this policy, you may discuss with other students the general principles required to understand this project, but the work you submit must be the result of your own effort. Plagiarism detection, and other systems for detecting potential malpractice, will therefore be used. Besides, if what you submit is not your own work then you will have learnt little and will therefore, likely, fail the final exam.
You must submit your project before the submission deadline listed above. Following UWA policy, a late penalty of 5% will be deducted for each day (or part day), after the deadline, that the assignment is submitted. No submissions will be allowed after 7 days following the deadline except approved special consideration cases.
Context:
For this project, imagine for a moment that you have successfully completed your UWA course and recently taken up a position for the Department of Prime Minister and Cabinet in Canberra with the Australian Federal Government. At first you were quite reluctant to leave Perth to move ‘over east’ and, more generally, wondered what use a new graduate with a heavy focus on computing, programming and data could be to this department. Regardless, the opportunity to gain experience in the ‘real world’ was too good, and although it is not quite your own multi-million dollar technology start-up, there was no way you weren’t taking up the offer.
Your first few weeks of orientation was a mostly blur. However, one thing you noticed was that any time you mentioned your skills in programming, and with Python1 in particular, to any senior bureaucrat, or even some of the savvier politicians, their eyes seemed to ‘light up’ and they suddenly became much more interested in whatever you
1 Actually their eyes are more likely to light up if / when you mention your skills in data science and machine learning and big data, for all of which Python is basically the foundational tool for.
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CITS1401 Computational Thinking with Python Project 2 Semester 2 2020
were saying to them. After reflecting on these experiences, maybe there would be some even more interesting opportunities for you in the near future?
However, for now you decide to put aside these, as it’s not like the work that you have been doing already has not been interesting, and this is what you need to focus on for today. At an early morning meeting with your immediate supervisor, you were told that the Government is very interested in reducing its spend on trying to understand what (and how) the Australian population currently thinks about it. Instead of spending millions of dollars calling randomised groups of Australian residents every quarter to ask about their opinions on various Government services, many senior bureaucrats have wondered for a while now whether there was any way to use the masses of freely available data on the internet to provide similar insights at a fraction of the cost.
It is within this context that your supervisor has asked you to develop a program, as a proof-of-concept, to demonstrate that it is possible to provide some of these insights at a much lower cost. At your meeting your supervisor noted that, for the proof-of-concept stage, the use of any ‘live’ internet data will not be possible without approval from the legal team (as well as possibly many others). This seemed like quite an obstacle until you thought back to one of your early Python units (maybe this one?) and remembered that there is an open source, freely available corpus collection of billions of recently crawled websites called the Common Crawl (http://commoncrawl.org/). More specifically the Common Crawl corpus consists of tens of thousands of files saved in a certain format (the WARC format, see below), each of which contains the raw HTML of tens of thousands of web pages from a web ‘crawl’ performed in the recent past. Being open source this data is free for you to use so with it you can immediately begin building your proof-of-concept.
The Project:
As your program is to be a proof-of-concept, both you and your supervisor decided that its scope should be kept as narrow as possible (but, of course, it must be broad enough so that it can successfully demonstrate some really good insights). For this reason, it was decided that your program is to focus only on providing four insights only:
1. How ‘positive’ is Australia generally?
2. How ‘positive’ does Australia feel towards their Government specifically?
3. How ‘patriotic’ is Australia compared with two other major English speaking countries – UK and Canada?
4. What are the most referred-to websites (domains) by all Australian websites (your team may want to use this information in the future to better understand how ‘influential’ each Australian web result is to your insights, i.e. highly-referred to web domains should be counted as more influential, and lowly-referred to web domains should be counted as less influential).
As outlined in the ‘context’ section, in order to generate these insights (which will be discussed in greater detail later in this document), your program will need to examine
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the raw HTML from large quantities of Australian web pages, and such information is available in WARC format from the Common Crawl.
The Common Crawl and WARC format:
The WARC (Web ARChive) format is a standard format for mass storage of large amounts of ‘web pages’ within a single file. The Common Crawl makes the results of their crawl freely available for download in this format (as well as the WAT and WET formats, which will not be used for this project). For this project we will use WARC files from the August 2020 crawl (https://commoncrawl.org/2020/08/august-2020-crawl- archive-now-available/). In order to access these files you need to download the “WARC files” list – which you can access by clicking on the “CC-MAIN-2020-34/warc.paths.gz” hyperlink in the table in the August 2020 crawl homepage.
Clicking on this link will download an archive, which, when opened, will contain a text file. Once you open the text file you can download any of the WARC files from the common crawl by appending https://commoncrawl.s3.amazonaws.com/ to the front of any of the lines of this file and pasting this full address into your browser.
A couple of notes about the Common Crawl WARC files as discussed so far:
• The file list and all Common Crawl WARC files are compressed using gzip. These files can be unzipped automatically if you are using Linux or Mac OSX. For Windows you will have to download a free application to do this – try 7-Zip: https://www.7- zip.org/.
• The Common Crawl WARC files are very large – approximately 900MB compressed and up to 5GB uncompressed. Each file contains approximately 45,000 individual crawl results.
Due to the size of the files above, this project has made available a massively cut down sample Common Crawl WARC file on LMS as well as Moodle server. It is expected you will use this file to get familiar with the format and for your (initial) testing of your project. However, your submission will be tested with other WARC files.
To start getting familiar with WARC files, it is recommended you download the sample file and open it in a text editor (for Windows, Wordpad performs better; you can also use Thonny). You will see that a WARC file consists of an overall file header, beginning with the text “WARC/1.0”, and the next time you see this text is to describe either a request (“WARC/1.0\r\nWARC-Type: request”), a response (“WARC/1.0\r\nWARC- Type: response”) or possibly a metadata or other type of WARC category (e.g. “WARC/1.0\r\nWARC-Type: metadata”). For this project we are only interested in WARC responses (“WARC/1.0\r\nWARC-Type: response”), as these are the only categories that contains the raw HTML data of the web page we are analysing.2
Looking into more detail at WARC responses, you can see that these are further broken down into three sections, which are separated by blank lines. The first is the WARC
2 Note the use of ‘\r’ with ‘\n’ to signify a line ending in the WARC (and HTTP) headers. This is a standard line ending code for text files saved with Microsoft Windows and some other scenarios. You will need to account for this when processing these headers.
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response header (beginning with “WARC/1.0”). The second is the HTTP header (usually beginning with “HTTP/1.1 200”) and the third is the raw HTML data (usually but not necessarily beginning with “”). For the purposes of this project, you can assume that the first block of text (before the first blank line) is the WARC header, the second block of text (after the first blank line) is always the HTTP header, and the third block of text (i.e. anything after the second blank line and before the next “WARC/1.0” heading) is the raw HTML that we need to analyse.
Taking into account the above, your program will need to be able to open a WARC file, discard or ignore the overall WARC file header, and then for each result:
1. ExtracttheURLfromtheWARCresponseheader(thisisstoredinthelinestarting with “WARC-Target-URI”)
2. Extract the “Content-Type” from the HTTP header. For this project we are only interested in responses that are of “Content-Type: text/html”. Any other types of HTTP responses can be ignored.
3. Extract the raw HTML for this result and store it in a data structure so that it is associated with the URL you extracted (in point 1).
Extracting Raw Text from HTML:
If you were to have a look at the raw HTML you have extracted in detail, you would see that it doesn’t quite (yet) look like nice words and sentences that you will be able to analyse to determine its “positivity” and “patriotism” as you are required to do for insights 1 – 3. In order to get your text to this point, you are going to have to perform some transformations on it, namely:
Removal of any HTML tags – any text between a ‘<’ character and a ‘>’ character you can assume is a HTML tag and needs to be removed before completing your analysis for insights 1, 2 and 3.
Removal of JavaScript code – before you remove your HTML tags above, you will also need to remove any text that is between the ‘’ tags (again only for completing insights 1 – 3). “Note that a ““) tag can have any amounts of whitespace or other text between the “” character and valid script tag that must be removed.
The Insights Themselves:
Some more details about what is required for each insight is below:
1. How ‘positive’ is Australia generally?
For this insight, both you and your supervisor are keen to understand how much Australian websites use ‘positive’ words compared to how much Australian websites use ‘negative’ words. It was decided that, for this insight, your program should produce a list with five items. The first and second items in this list are the total count of positive words and negative words respectively within the raw text for all Australian web pages
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that were in the WARC file provided to your program. The third item of the list should be the ratio of positive words to negative, which can be calculated by dividing the former by the latter. The fourth and fifth items should be the average number of positive words and negative words respectively found in the typical Australian web page.
To assist you in this duty, your supervisor has provided you with a list of common positive English words, and a list of common negative English words. You can find these lists as text files on LMS and Moodle Server. For this project you can assume that any words that are not in either of these lists should not be included as part of the positive or negative counts.
Note in order to produce accurate results here, you will have to make sure that your program counts the appearance of any positive and negative words in your text regardless of the word’s case (uppercase / lowercase or a combination of the both) and any punctuation at the start, end or within each word itself (e.g. commas, full stops, quotation marks, etc.) – “- in fact it is recommended you remove all punctuation from your text before performing this step (for now assume every non-alphanumeric character visible on a standard ANSI keyboard is a punctuation character that needs to be removed, if you have bought your computer in Australia or the US then it is very likely you will have an ANSI keyboard, if not then you can easily find out what punctuation an ANSI keyboard contains through a Google image search).
Note the above analysis should be performed on Australian websites only. For this project assume that a website will always an Australian website if, and only if its domain name ends in a ‘.au’ (domain names are discussed in more detail in insight 4).
2. How ‘positive’ is Australia towards its Government?
As well as calculating how ‘positive’ Australia is in general, the second outcome of this project is to determine how ‘positive’ Australia is towards its Government. In order to determine this your program should examine every sentence that contains the word ‘government’ for any positive or negative words. You and your supervisor decided on the following rules for any sentences containing the word ‘government’:
• If the sentence has only one or more positive words then it should be counted as a ‘positive’ sentence.
• If the sentence contains one negative word then it should be counted as a ‘negative’ sentence however if the sentence contains two negative words then it should be counted as a ‘positive’ sentence (i.e. it is likely that the writer has used a double negative). If the sentence contains three or more negative words then it should be counted as a negative sentence.
• If the sentence contains a combination of positive and negative words (or no positive or negative words) then it should not be counted as either positive or negative.
As with insight 1 your results should be provided in a list with the first and second items being your raw positive and negative counts, and the third item being the ratio of positive to negative counts, and the fourth and fifth items being your average number of positive sentences and negative sentences per web page respectively.
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Also as with insight 1, the above analysis should be performed on Australian websites only. In addition, the same directions with regards to the word’s case and punctuation applies also, but you may wish to delay removing any sentence-ending punctuation characters (see below) to ensure you are able to split your raw text for the result into individual sentences first.
For this section you can assume that a sentence is any number of words ended by a sentence-ending character (a full stop, a question mark or an exclamation mark).
3. How ‘patriotic’ is Australia compared with the other major English speaking countries
For this insight you are required to determine how often the word “australia” appears in the raw text of your Australian websites compared with how often the other country’s names appear in their web sites, specifically focussing on two other major English speaking countries – Canada and the United Kingdom (who both have their own unique TLDs):
• For Canada your program should determine how often the word “canada” appears in the raw text for any URLs whose domain name ends in “.ca”.
• For the United Kingdom your program should determine how often the word “uk” and the phrases “united kingdom” and “great britain” appear in the raw text for any URLs whose domain name ends in “.uk”.
All of the insights are to be calculated as percentages with the following formula:
(total number of occurrences of all words / phrases for the country) / (aggregate number of words of every web result’s raw text for that country) * 100.
These percentages are then to be provided in a list in the order of [Australia, Canada, United Kingdom].
The same directions from insight 1 with regards to the word’s case and punctuation removal applies to the words you will examine in this insight also.
4. Web domain links and counts
For every Australian web page in your WARC file, your program should count every domain name that it links to. A domain name is the part of a URL that refers to the root
site, for example the domain name for the link “https://www.google.com.au/example/testing.html” is “www.google.com.au”. For this project you can consider that a link in to web page only exists when it appears within a tag that contains the “href” attribute within the page’s raw html, for example,
“” or
‘’ or ‘’.
Your program should examine each link that each of your Australian web pages refers to, extract only its domain name (that includes any subdomains, including ‘www’), and count the occurrences of these domain names across all of your Australian results. Any
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links that do not start with a ‘http://’ or a ‘https://’ can be ignored. Your program should return the top 5 most occurring domain names and their counts in a list of tuples with the format: [(domain_name, aggregate_count), …]. The tuples in the list should be in descending order by count. In case of tie in their counts, rank them in ascending alphabetical order.
Note URLs are case-insensitive, so all domain names (and indeed all text within “>> gen_pos, gov_pos, pat, top_links = main(“warc_sample_file.warc”,
“positive_words.txt”, “negative_words.txt”) >>> gen_pos
[1238, 586, 2.1126, 26.913, 12.7391]
3 This list is sourced from Minqing Hu and Bing Liu. “Mining and Summarizing Customer Reviews.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, Washington, USA, (online access: https://gist.github.com/mkulakowski2/4289437)
4 As above (online access: https://gist.github.com/mkulakowski2/4289441)
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