United Nations Goals: Language Analysis
Web Scrapper [in R], JSON
In September 2015, the United Nations announced the 2030 Sustainable Development Goals as a continuation of the Millennium Development Goals initiative. The implications of having the member states and a large portion of the world committing to these common goals are far reaching. This initiative largely resembles a contract, and as such, every word matters.
I wanted to take the goals and look at them from an analytical perspective by using a POS (part of speech) Tagger. This, in a sense, allows me to reverse engineer the thought process that went on in formulating the text. It becomes evident, by the use of identical action verbs across the 17 goals, as well as by the nuances of the adjectives used, that the UN put in a lot of editing effort in equalizing the weight that the language carries across the goals.
On the other hand, when we look at the descriptive statistics (word frequency) we see the story from a different angle. I will let you draw your own conclusions but I found the process of looking at the world’s agenda for the next 13 years from this perspective rather intriguing.
There is a version 2 of this project in the works that will fix some bugs, improve the selection logic, and put forward a much more rigorous analysis.
Current status: Ongoing
The Part of Speech Tagger can be applied to the titles of the goals as well as their detailed description
All 17 goals
The Part of Speech Tagger selection
Match all verbs
Now matching all nouns
The detailed view of the "Achieve gender equality and empower all women and girls" goal with all verbs isolated
There is also an "In Numbers" summary that shows the frequency of usage of every word used more than once