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KIT501 ICT Systems Administration Fundamentals
Task
Unix shell scripting and cricket.
Test your knowledge at scripting to combine, explore and analyse big data! You will be required to use Unix-like shell commands to complete the questions relating to the cricket_tweets datasets. Information about the dataset is located at the end of this file.
Using the dataset, answer the following 11 questions. You will need to use Unix shell script to manipulate the file and answer the questions. Please include copies of all the Unix bash shell command line scripts (screenshot is fine).
Questions:
1) Decompress the file. How big is it before and after compression (bytes)?
2) Looking at the data, what delimiter is used to separate the columns in the dataset and how many columns are there?
3) The first column is the unique identifier for each tweet. What are the other columns?
4) How many tweets are there in the dataset?
5) What is the date range for the tweets in this dataset? (Assume that the lowest unique identifier data is the earliest)
6) How many unique tweet messages are there?
7) How many tweets don’t have alphanumerical text in their message?
8) When was the earliest mention in the messages of “Don Bradman” and which account wrote it?
9) How many times is “Australia” mentioned in the messages? How did you find this? (Do not ignore the case – so only “Australia”, and not “australia” or “AUSTRALIA”)
10) How many tweet messages discussed Australia and England? How many discussed Australia but not England?
11) Which tweets have the greatest positive, negative or compound sentiment scores? (find one tweet ID for each).
12) Find out which month had the most tweet messages about Ashes.
Information about the dataset:
The dataset, cricket_tweets.zip, contains tweets from a range of twitter accounts discussing cricket. The data for each tweet includes information like a message, an author account and the date/time at which it was sent, plus further information about the tweet and the account. Additionally, the tweet data has also been run through a sentiment detection algorithm so the data has scores to indicate how positive, negative or neutral the words are as well as a compound sentiment score.
Hint: Since the dataset is about 20MB, use a Linux machine for this, Mac terminal, or Cygwin on a Windows machine.
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