DC: Small: Cross-Language Bayesian Models for Web-Scale Text Analysis Using MapReduce

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The Web promises unprecedented access to the perspectives of anenormous number of people on a wide range of issues. Turning thatstill untamed cacophony into meaningful insights requires dealing withthe linguistic diversity and scale of the Web. Most current researchfocuses on specialized tasks such as tracking consumer opinions, andvirtually all current research treats the Web as both monolithic andmonolingual, ignoring the variety of languages represented and therich interplay between topics and issues under discussion.This project moves the state of the art forward by focusing on two keychallenges. First, highly-scalable MapReduce algorithms forlinguistic modeling within a Bayesian framework, making use ofvariational inference to achieve a high degree of parallelization onWeb-scale datasets. Second, novel Bayesian models that learnconsistent interpretations of text across languages and a wide rangeof response variables of interest (for example, views on an issue,strength of emotion relative to an event, and focus of attention).The techniques developed in this project will be demonstrated on largecrawls of Web pages and blogs. Potential applications for thesetechnologies include helping a schoolchild learn that people indifferent countries may view some issues very differently, helping apolitician understand how constituents are reacting to proposedlegislation, or helping an intelligence analyst understand how publicopinion is evolving in a hostile country.For further information see the project Web page:http://www.umiacs.umd.edu/~jimmylin/cloud-computing