Thoughtful Machine Learning: Sentiment Analysis Using Support Vector Machines in Ruby
Date: This event took place live on November 11 2014
Presented by: Matthew Kirk
Duration: Approximately 60 minutes.
Questions? Please send email to
This webcast is no longer available for viewing.
"That's stupidly awesome" or "You're such a jerk :)"
The above is obviously positive, but how would you train a computer to figure that out? So much of our language is contextual and has subtle hints of sentiment that this is a tough problem in natural language processing.
Though there is a great algorithm called Support Vector Machines that can find a close solution! And there's a great Ruby library for you to use as well.
Join us for this webcast where we'll go detecting sentiment in tweets using support vector machines. At least join us for the various bouts of swear words and confusing lexicon of the English language.
About Matthew Kirk
Matthew Kirk holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He started Modulus 7, a data science and Ruby development consulting firm, in early 2012. Matthew has spoken around the world about using machine learning and data science with Ruby.
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