Showing posts from February, 2014

Followup to Superbowl Tweets

Comparison CloudI classified the tweets on the basis of hashtags as either Bronco or Seahawk fans
Bronco Fans :    #GoBroncos","#BroncosNation","#BroncosFan","#GoManning","#PeytonManningRocks", "#BroncosWin"
Seahawk Fans:  #GoSeahawks","#SeahawksNation","#SeahawksFan","#GoSeattle","#SeahawksWin","#SeattleSeahawksRule","#CrushBroncos","#CrushManning","#Manningchokes

Broncos Fan Tweets  Geo-plot  

Seahawk Fan Tweets  Geo-plot 

Tweet Sentiment Comparison
I took a rolling mean of 100 tweets for Broncos & Seahawk fans. The excitement of Broncos fans was pretty short lived and as the game progressed, that blue line for Seahawks consistently showed a better sentiment score than the red line for Broncos. 

By the end of the game, there were rapid spurts tweets with Broncos hash tags that were loaded with negative words and the red line tells that story.

SuperBowl 48 Tweets

Analysis of Tweets from  Superbowl 48
Wordcloud from tweets with  #superbowlcommercials

CocaCola's  "America is Beautiful" Maserati                                     Bud Light                                  Budweiser                                 Radio Shack                              Cheerios                                   

Bruno Mars Wordcloud Bruno Mars Tweet Sentiment
Took a rolling mean (100 tweet's) of the sentiment scores to get the time series below. These were for all the tweets where the text contained "Bruno"
Top  30 Tweeters

Y Axis : # of Tweets from a user Geo PlotsAbout 4.1%  of total tweeters  had their geo-coordinates on.
Centered   - Nor…

Geoplotting Twitter Users can get creepy

So, about 3-4% of people on average seem to Tweet with their geo-coordinates on the mobile devices turned on. Thanks to Google street view and ggmap package, that information can be precious to someone running a marketing campaign, new retail store opening, happy hours at bar/restaurant, or someone who is very curious .

Around Black Friday 2013, I started getting a tweet grab of people tweeting with #Blackfriday, #Blackfriday 2013deals to do some trend and sentiment analysis of major brands like Amazon, Target, Walmart, Sony, Dell etc.

As I started to dig deeper to see people who were constantly tweeting good/bad with co-ordinates turned on, I saw this user close to Gainesville, Florida who was tweeting almost every 5 minutes. At the street map level, I could  see this person stopping at stores like Kohls, Walmart, BestBuy and talking about deals. Then I saw some tweets coming from a residential address about finally reaching home, how much shopping the user did, what door busters th…

Facebook Page User Stats & Engagement

Started to play around with the R package "RFacebook"   to connect to Facebook's API's and access social graph data.
I am posting some of the social graph visualizations that I created using Gephi and ggplot2  ( visualization package in R)

This is a heterogeneous social graph of Facebook page  for my favourite RSS news reader Feedly

Hetrogeneous Graph : The nodes (spheres/dots) on the graph to the left  are
1. Facebook users
2. Facebook posts

The edges on this graph depict connections between nodes, and for this particular graph, I looked at the "likes"

The nodes have  also been color coded where blue represents male, pink represents female, and the green is undisclosed. The green nodes are the Facebook page posts by Feedly Facebook admin.
The nodes sizes are also scaled proportionally by the in-degree (# of people who liked a post) , so the larger green nodes were the posts the generated a …