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Visualizing Information

An article presenting how information could be visualized.
© Luleå University of Technology

During week one, we introduced different data sources such as the European Commission and Statista. In this article, we attempt to analyze one of them.

We encourage you to actively participate in the execise using Orange. If you are unable to perform the exercise you can still follow along below.

Remember, to analyze the data, we need a tool. The tool which we will use is Orange. There are several other alternatives you may also use, if you are used to or find another that you prefer.

Steps to analyse the dataset

  • Open Orange on your computer
  • Download the data. You can access the data set here.
  • Now, we assume you know where you have stored the dataset “1377884570_tweet_global_warming.vsc” in your computer. You will be asked to refer to it later in the steps below
  • Right-click on the white Canvas area and type “CSF File Import” and then add. Do the same to add both the Corpus and Word Cloud Widgets. Connect the widgets so that they look like the following image:
  • Double click on the CSV File Import widget and direct it to where you have stored the downloaded file
  • Save by clicking on File menu, then Save it under the name “Task2”
  • Double-click on the Word Cloud widget and you will see a visualization image like the below:

wordcloud example. Displays the frequency of the word in the corpus

The word cloud displays the words in the corpus whereby their size denotes the frequency of the word in the corpus. It is one way to visualize a large amount of text in one figure. It is to be noted that we tried to simplify the example. Those who want to scrutinize the above image can add the Process Text widget between Corpus and Word Cloud and then use the newly added widget to get rid of the stop words, numbers, signs e.g., hash & brackets, etc.

What have you learned?

  1. Textual data can be easily and quickly visualized
  2. The above analysis can be optimized!
© Luleå University of Technology
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Data Science for Climate Change

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