DATASCIENCE *PROJECTKEVIN BLUER

DSRUPTION

http://www.dsruption.com/

TRENDS TECHNOLOGY

http://www.dsruption.com/trend/wearable-computing

GOALS

Derive Insight from Dsruption (www.dsruption.com)

Focus on establishing company (startup) momentum & insights

#1 Article popularity (FB / Twitter shares)

#2 Auto generation of article tags

FEATURES

dsruption.activity, 691 documents (744 KB)

dsruption.articles, 14022 documents (125.61 MB)

dsruption.comment, 43 (40 KB)

dsruption.companies, 524 (3.65 MB)

dsruption.tags, 329 (40 KB)

dsruption.trends, 32 (140 KB)

dsruption.users, 39 (632 KB)

TECHNOLOGIES

MongoDB

JavaScript and Node.js

D3.js

Hadoop

Python

Facebook and Twitter API’s

ARTICLE POPULARITY

IMPORTINGTWEETS & SHARES

http://www.dsruption.com/dwolla/json-social

SIMPLE D3.JS VISUALIZATION

http://www.dsruption.com/dwolla/visualize

COMPANY TAGS FROM ARTICLES

HADOOP -> MONGO

http://www.dsruption.com/dwolla/articles

http://www.dsruption.com/data/dwolla.json

BEAUTIFUL SOUP<p><ul><li><span style="font-family: arial;"><i>100,000 refrigerators and freezers have now made their way through the revolutionary UNTHA Recycling Technology system</i></span></li><li><span style="font-family: arial;"><i>Innovative recycling system reduces landfill waste and greenhouse gas and ozone-depleting substance emissions</i></span></li><li><span style="font-family: arial;"><i>Initiative has diverted 5.5 million pounds of material from U.S. landfills<b><a href="#_ftn1" name="_ftnref1">[1]</a></b></i></span></li></ul> </p><p style="text-indent: -0.25in;"><i><b><br/><br/></b></i></p><p><div><br/><div id="ftn1"> </div> </div></p> 

  

100,000 refrigerators and freezers have now made their way through the revolutionary UNTHA Recycling Technology systemInnovative recycling system reduces landfill waste and greenhouse gas and ozone-depleting substance emissionsInitiative has diverted 5.5 million pounds of material from U.S. landfills.

LOTS OF NOISE

http://www.dsruption.com/dwolla/words

EXCLUDE NOISE

count: 252, word: "Dwolla”

count: 73, word: "money”

count: 45, word: "photo”

count: 44, word: "people”

count: 42, word: "pay”

count: 39, word: "payment”

count: 35, word: "payments”

count: 34, word: "business"

WHAT’S NEXT?

Sentiment Analysis (both on the articles / comments)

Integration of Additional Datasets (Crunchbase, etc)

Broader Visualization

THANK YOU