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Leverage Social Signals to Business Advantage
April 29 & 30, 2015San Francisco, CA
JW Marriott, Union Square
Current Speakers Include: Staff Software Engineer, Google Chief Data Scientist, HireVue Chief Technical Officer, metaMind Staff Technical Program Manager, Twitter Staff Software Engineer, LinkedIn Head of Text Analytics, Thomson Reuters Manager, R&D Machine Learning, Bloomberg Senior Data Scientist, Glassdoor Senior Research Scientist, Yahoo! Director, Data Science & Engineering, MyFitnessPal Director, Customer Analytics, Toyota Financial Big Data Architect, Skype & Many More..
Past Delegates Include Vice President, Analytics - Amazon Data Scientist, - Twitter Director, Analytics - Yammer Director, Engineering - Google Sr. Director - eBay Exec Director, Marketing - EA Electronic Arts
Who Will You Meet?There is no question that IE. provides the gold standard events in the industry and will connect you with decision makers within the analytics industry. You will be meeting senior level execut ives from major corporations and innovative small to medium size companies.
Job Title Of Attendees
Global Head/ Head
1000+ Employees300-999 Employees50-299 EmployeesLess than 49 Employees
Company Size Of Attendees
25%56% 81%Attendees are
companies with at least 300
Attendees are at Director level or above
F TI L
With the rise of social media in recent years online opinion is now a key indicator for businesses as to their customers wants and needs. The challenge remains to accurately analyze behavior and sentiment in order gain acrucial advantage over competitors.
By using natural language processing, text analytics and computational linguistics organizations can begin to filter through the noise and begin to identify the relevant
content within conversations. Offering more clarity than basic text analytics, sentiment analysis isthe way forward for organizations who want to tap into the mind of the consumer.
Illustrated intermittently with case studies, interactive panel sessions and deep-dive discussions, this summit offers solutions and insight fromsome of the leaders using sentiment analysis to great effect.
About The Summit
Current Speaker Information
Benjamin TaylorChief Data ScientistHireVue
I am "cloud boy". I have had experience setting up large GPU compute clusters the manual way with ip tables, NFS mounts, ssh keys, ssh cloud configs scripts, service configs, and all of the unexpected troubleshooting that comes with that. It sucks. Now I have seen the light with virtual cloud solutions like AWS and I am in heaven! I can do what use to take me weeks in minutes. Need a 1000 node hadoop/cassandra/mysql cluster/sun grid/cluster? No problem! Need dynamic expansion/contraction? NO PROBLEM! Need a cloud AI optimizer? Oh wait, that doesn't exist yet... don't worry I am on it.
Latest Text Feature Creation & Reduction Methods Demonstrated on Interview Ranking Models
Ben will demonstrate in detail some of the many text features beyond just simple word or n-gram use. Automated feature engineering will also be demonstrated using deep learning techniques. Finally, after generating 100,000s of features how do you reduce the features effectively to reduce computation costs and increase accuracy. Metaheuristic feature reduction on distributed systems will be demonstrated to address automated feature reduction. Lastly, examples and data analysis will be presented with real interview data on one of the world's largest digital repositories of recorded interviews.
Vita MarkmanStaff Software EngineerLinkedIn
I am currently employed as a Staff Software Engineer at LinkedIn, where I work on various natural language processing applications such as performing sentiment a n a l ys i s of c u s to m e r f e e d b a c k , m e m b e r d a t a standardization, and context-sensitive spam detection. Before joining LinkedIn, I was a Staff Research Engineer at Samsung Research America, where among other projects, I worked on extracting topic-indicative phrases from a stream of closed caption news data in real-time and text-mining customer support chat-logs for common issues customers experience.
Mining Topic-Based Sentiment in Customer Feedback at LinkedIn
This talk addresses the topic-based sentiment analysis of customer support feedback focusing on the following questions 1) how do we find the most relevant topics of a product in question 2) how do we ensure to attribute sentiment to these specific topics as opposed to the feedback as a whole 3) how do we leverage natural language processing tools such as key phrase extraction, synonym identification, and summarization to make the obtained topic-sentiment information best suitable for human consumption. The model proposed here is extendable to mining sentiment in reviews or any other sentiment-bearing text.
Current Speaker Information
Alessandro GagliardiSenior Data ScientistGlassdoor
Alessandro Gagliardi is a Sr. Data Scientist at Glassdoor and an instructor of Data Science at General Assembly in San Francisco. At Glassdoor, he uses big data and machine learning techniques to predict salaries and present the best opportunities to job seekers. Prior to that, he worked for Path, analyzing terabytes of customer activity logs to provide business insights for product development. Alessandro received his B.A. in Computer Science from the University of California.
Using NLP to Improve the Job Hunting Experience
Alessandro will discuss his work in applying machine learning techniques to extrapolate salaries from user generated content, as well as providing statistical analysis of search metrics to provide a better job hunting experience for Glassdoor's customers.
Richard SocherChief Technical OfficermetaMind Institute
Richard Socher is the co-founder and CTO of MetaMind, a young machine learning company focused on pushing the state of the art in AI and making it accessible to many people. More concretely, MetaMind usesdeep learning and m a c h i n e l e a r n i n g t e c h n i q u e s t o s o l v e m a n y differentnatural language processing and computer vision problems. In 2014,Richard received his PhD from Stanford University working with Chris Manning and Andrew Ng. He was awarded the 2011 Yahoo! Key Scientific Challenges Award and a 2013 "Magic Grant" from the Brown Institute for MediaInnovation.
Deep Learning Applied to Real Problems
Sentiment analysis is both linguistically interesting and crucial to business intelligence. In this talk, I will first describe overly simple methods forsentiment analysis that have been used in the past. Next, I will describe current methods of sentiment analysis and demo an easy to use tool for text and sentiment classification: etcml.com. In the third part, I will introduce more sophisticated models based on recursive deep learning which I believe will eventually supersede currently used algorithms thanks to their improved performance.
Jim SkinnerStaff Technical Program ManagerTwitter
Jim Skinner has spent the last 20 years developing distributed platforms that enable the consumption of digital content. During his 14 years at Microsoft Jim helped bring MovieLink.com, Korea Telecom's MegaTV and Microsoft's IPTV (sold as AT&T U-Verse, British Telecom Vision, Deutsche Telekom T-Home, etc.) to life. At Netflix Jim focused on video encoding, the content ingestion pipeline and content recommendations. Jim is expanding the functionality of the Twitter client by providing technical program management of the Twitter Cards platform.
The Business of Big Data
Companies like Gnip, Google, IBM and Metamind are turning data analytics and machine learning into viable products. Will these commercial offerings overtake the "free to use" tools such as the Torch framework and Stanford's Classifier? This technical talk will focus on the marketplace for big data (who is buying what), the vendors trying to survive in that marketplace and their current product offerings. You'll learn about the difference between free and paid access to big data, the limitations of existing machine learning "products" and the opportunity this presents for data scientists.
Zornitsa KozarevaSenior Research ScientistYahoo!
I am interested in natural language processing such as large-scale knowledge acquisition from the Web and its app l i cat ion to rea l wor ld prob lems l i ke name disambiguation, entity extraction and paraphrase acquisition. I am also interested and have worked on machine learning, social media, sentiment analysis and graph-based algorithms for text processing.
Multilingual Affect Polarity and Valence Prediction in Metaphors
Understanding metaphor rich texts like "Her lawyer is a shark",