For our November Meetup, we're very happy to have Maksim (Max) Tsvetovat from local analytics consulting firm Deepmile Networks, talking about extracting sentiment from Twitter data. Although the idea of using billions of tweets to learn about opinions is appealing, getting it to work in a compelling and valuable manner has been fraught with difficulty. Max will bring us up to speed, and discuss a method that works well for certain domains.
Notes: We're back at Google for this event! And we'll be continuing our experiment with informal pre-event themed networking -- please come early to meet and chat with people interested in Natural Language Processing!
- 6:30pm -- Networking and Refreshments (Discussion theme: NLP)
- 7:00pm -- Introduction
- 7:15pm -- Max's presentation and Q&A
- 8:30pm -- Post presentation conversations
- 8:45pm -- Adjourn for Data Drinks (location TBA)
In this talk, I will describe a new method for estimating sentiment in online speech. This method does not rely on pre-defined lists of "good" or "bad" words -- but, rather, measures affinity toward a subject, brand, politician, etc. by locating and measuring psycholinguistic similarities between speakers and producing aggregate sentiment statistics. This method is ideally suited to understanding sentiment toward politicians, journalists, advertisers -- anyone that produces large amounts of direct speech. While this limits the domains in which this method is applicable, its accuracy
Max is the Chief Technology Officer at DeepMile. He has a PhD from Carnegie Mellon University and is currently a Research Assistant Professor at George Mason University where he teaches Social Network Analysis. He is widely published in computer science, organizational theory and social network journals, and is a regular presenter at industry conferences. To learn more about Max and his research, you can explore his website -- www.tsvetovat.org. You should also buy his book, Social Network Analysis for Startups.