Helena S Chapman Steve Atkin

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Presentation transcript:

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

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

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

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

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

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

Demo Time Where to find more information: http://www.ibm.com/developerworks/cloud/library/cl-watson-translatetwitter-app/

1990 Source: The Atlantic Source: The Atlantic

2010 Source: The Atlantic Source: The Atlantic

2005 Source: The Washington Post Source: The Washington Post

2013 Source: The Washington Post Source: The Washington Post

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