1. Helena S Chapman Steve Atkin
Rapid Translation and Twitter: Crunching the Hunger in Understanding Your Digital Data
Helena S Chapman
Steve Atkin

2. The Power of Crowd Middle East has the fastest internet usage growth
Social media marketing spending increases more than twice by 2019
75B things by 2020
1.35B monthly active users on Facebook (4Q2014)
Breaking news moves from TV to Twitter
LinkedIn is used in 200
Countries and territories
66% users access social media via phones or tablets
ME growth:
6000 tweets every second = 500M tweets a day
30T Web pages in Google index, 2013

3. Facts Social media impacts businesses internally and externally
Massive (unstructured) data volume for analytics across languages
There are security, compliance, and IP protection issues involved

4. More Facts
Through social data analytics, enterprises created 13% more revenue through digital channels
3.7 times more likely (than their peers) in developing new revenue streams
Twice more likely to leverage cloud to transform their business models
2.1 more times likely to link customers, partners, and employees to better engage the market
Twitter: 3rd largest country (virtual)
More than one billion users; 77% outside US

5. What? Create an application to crawl for a key phrase in tweets
Use Watson language identification to find source language of the tweets
Leverage Watson MT to translate located tweets
Perform sentiment analysis on the results

6. How? IBM Bluemix Twitter Application Management and Twitter4J
Watson Machine Translation
Watson Language Identification
Liberty for Java runtime
Twitter Application Management and Twitter4J

7. Demo Time Where to find more information:

8. 1990
Source: The Atlantic
Source: The Atlantic

9. 2010
Source: The Atlantic
Source: The Atlantic

10. 2005
Source: The Washington Post
Source: The Washington Post

11. 2013
Source: The Washington Post
Source: The Washington Post

12. What’s your idea?