Social Media Analytics – 5 Featured Sessions at TAW San Francisco

Social Media Analytics at
Text Analytics World San Francisco

Social Media Analytics at
Text Analytics World San Francisco

Text Analytics World, March 6-7 in San Francisco features cutting edge social media analytic methods and techniques – Check out these five TAW sessions, plus two more at Predictive Analytics World (colocated):

Modeling Collective Mood States Using Social Media Analytics
	Johan Bollen, Associate Professor, Indiana University
Professor Bollen has gained a great deal of noteriety for discovering financial indicators from social media, i.e., predicting the stock market from Twitter feeds. Learn how Professor Bollen ‘s teamhave analyzed large- scale Twitter data tBollen ‘s team have analyzedo yield accurate measurements of the public’s mood state which in turn have been shown to contain predictive information with regards to the Dow Jones Industrial Average. In addition you will learn how the team performed an analysis of longitudinal changes in individual user sentiment over hundreds of thousands of Twitter users to study the effects of social networking relations to evolving user mood states.   View the complete, detailed session description here

Predicting Real-World Events and Sentiment via Social Media Analysis
Rishab Ghosh, Co-Founder & Vice President of Research, Topsy Labs

Throughout the Mid East uprisings social networks such as Twitter have played a central role to communicate, organize and amplify uprising activities. This session will demonstrate how real-time search and analytic technology applied to social media can predict events and trends using real-word examples from the Mid East and elsewhere.

Developing Predictive Analytic Solutions Using Social Personas Derived from Social Media
Kumar Subramanyam, Senior Solutions Architect, Hewlett-Packard

Over the years, predictive analysis has relied on syndicated data to augment a company’s internal data. Predominantly, these solutions have been wrapped around historical customer behavior data. But over the years, there has been tremendous growth in social media content, i.e. reviews, blogs, tweets etc. These social media sources contain numerous leading indicators that can be used towards anticipating behavior. This presentation focuses on techniques used towards developing social personas from these indicators and then in turn using these social personas towards enabling predictive analytic solutions.

Predictive Social Marketing – Sentiment Forecasting and Impact on Success
John Bates, Product Manager, Predictive Marketing Solutions

Every summer, music fans worldwide look forward to one of the biggest music events of the year—the MTV Video Music Awards (VMAs). This year’s VMAs turned out to be one of the world’s largest, simultaneous social viewing experiences ever. Leading up to this year’s VMAs, MTV marketers set out to grow the brand’s social media presence and drive awareness. With 85 million MTV Facebook fans and more than three million Twitter followers—the stage was set for a firestorm of conversation and sharing. In order to assist MTV in their primary goal of gaining deeper insights into relationships between social activity and engagement with digital content, we combined Twitter and MTV.com data streams with text mining and predictive analytics techniques. As marketing continues to develop, sentiment forecasting will be critical to success in optimizing published content for both publishers and advertisers.

Behaviorals: Using Twitter & the Social Internet to Obtain Above Market Returns
Randy Saaf, CEO, AlphaGenius

Behaviorals is not an introduction to behavioral economics or about knowing yourself as an investor. There are lots of books on behavioral economics that give thorough analysis of why investors, as people, make irrational decisions and how to not let yourself fall victim to your own human emotions while investing. Instead, behaviorals is about studying other people as emotional investors and using text analysis of the social Internet to measure mass psychology to obtain above market investment returns.

Predictive Social Media Analytics at PAW (Co-Located Event):

Case Study: Social Media Research Foundation
Crowd Photography for Social Media
Marc Smith, Social Media Research Foundation

Case Study: Real-world examples in Financial Services, Emergency Response Exploring Social Data: Use Cases for Real-World Application
Chris Moody, Gnip

View Text Analytics World San Francisco Agenda and Session Details

TAW San Francisco’s agenda covers hot topics and advanced methods such as blackbox trading, customer service and call centers, decision support, document discovery, document filtering, financial indicators from social media, government applications, insurance applications, knowledge discovery, marketing and branding, product launching, sentiment analysis, social data, social media applications, text analytics software, topic discovery, voice of the customer, and other innovative applications that benefit organizations in new and creative ways.

Want to learn more? Download the conference guide for a comprehensive look at what we have lined up.

Register now – Bring the team and realize savings. Each additional attendee from the same company registered at the same time receives an extra $200 off the Conference Pass