Dt: 05th March 2018
OK as promised I will be sharing the Tableau vizzes from the @arsenal search on twitter feed.The twitter extract was taken after the Carabao Cup Final which unfortunately Arsenal lost.
Here is how twitter reacted to the result .I have listed some tweets which were liked by twitterrati and retweeted the maximum times
Tweets
I extracted around a few thousand tweets and this is the frequency of the tweets over a timeline
Timeline
A quick plot of the location of the people who tweeted will give you an idea of how the English Premier League & the Arsenal football club is followed all around the world.You would be surprised to know that after US, UK & the Netherlands the maximum tweets came from India, Indonesia, Morocco & Kenya. Now thats where the merchandise is most likely to sell.Unfortunately the club is not in India in a big way in terms of selling merchandise.Something that they can do with FDI rules being relaxed in the retail space.
OK as promised I will be sharing the Tableau vizzes from the @arsenal search on twitter feed.The twitter extract was taken after the Carabao Cup Final which unfortunately Arsenal lost.
Here is how twitter reacted to the result .I have listed some tweets which were liked by twitterrati and retweeted the maximum times
Tweets
I extracted around a few thousand tweets and this is the frequency of the tweets over a timeline
Timeline
Mapped
Someone asked me about sentimental analysis and the most popular hashtags. I tried to analyse this by splitting the extracted tweets and getting the first hashtag on each one of them.Of course with some more SQL you could get all the hashtags but the first hashtag was good enough for me.This is how the hashtag cloud came up.
Hashtag Cloud
Finally I wanted to know how we could summarize info on various twitter vizzes onto one amazing dashboard which could be presented to the executives of these institutions.This is what I came up with. Of course the more creative ones will get better it better designed :-)
Dashboard
Well this is how amazing it can be. If someone tells you that they have expertise on R and Big Data and want to demonstrate solutions , you got to ask them if they could get this done on a bigger scale.If they can do this then they have got it.As they say the proof of the pudding is in the eating :-)
Thats it for now .Will be back with more interesting features on Tableau reporting.
Signing off
Note:If anyone knows what #coyg means then please leave me a comment .New generation language :-|
Someone asked me about sentimental analysis and the most popular hashtags. I tried to analyse this by splitting the extracted tweets and getting the first hashtag on each one of them.Of course with some more SQL you could get all the hashtags but the first hashtag was good enough for me.This is how the hashtag cloud came up.
Hashtag Cloud
Of course #Wengerout was one of the more widely used hashtags .Fortunately/unfortunately he is not going anywhere :-)
Finally I wanted to know how we could summarize info on various twitter vizzes onto one amazing dashboard which could be presented to the executives of these institutions.This is what I came up with. Of course the more creative ones will get better it better designed :-)
Dashboard
Well this is how amazing it can be. If someone tells you that they have expertise on R and Big Data and want to demonstrate solutions , you got to ask them if they could get this done on a bigger scale.If they can do this then they have got it.As they say the proof of the pudding is in the eating :-)
Thats it for now .Will be back with more interesting features on Tableau reporting.
Signing off
Note:If anyone knows what #coyg means then please leave me a comment .New generation language :-|
Hey Ashwin. Loving your tableau vizzes series. It got me really curious. Looking forward for more features of tableau.
ReplyDeleteAlso #COYG is Come on you Gunners! Should just be replaced with Wenger out though :(
Thank you for your information.Nice post
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