Challenge #89: Analyzing Social Data

[binuacs]

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[TheOC]

I had some fun finding which hashtags had the most 'potty mouths'! Fair warning, both the workflow, and the data contain a lot of swears! I would definitely not recommend downloading unless you're okay with that.

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Turns out the globalpeoplesummit hashtag gets the most heated, although given the size of the data I would give that badge to UNGA_

[AndrewS]

#89

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[Luke_C]

attaching some solutions

[Sashikumar]

My Solution.

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[rcarog]

Solution attached.

[mpennington]

My initial approach was to parse and clean hashtags and dates. I also took the top 200 locations. Data was very noisy, but it was doable to manually add regions to the top 200 locations. Not precise, but as good as can be expected with user created location data. 

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[Shifty]

Quite interesting to see what days of the week people tend to tweet and what time of the day. #ungaunga

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[Shifty]

Why am I not surprised?

[bkclaw113]

Sorry I may be in the minority but I really do not like the  "open" nature of this challenge, if I wanted ambiguous requirements I get plenty in my job.