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SUMMARY
The recent massive growth in online media and the rise of user-authored content (e.g weblogs, Twitter, Facebook) has lead to challenges of how to access and interpret these strongly multilingual data, in a timely, efficient, and affordable manner. Technically, streaming online media pose new challenges, due to their shorter, noisier, and more colloquial nature. Moreover, they form a temporal stream strongly grounded in events and context. Consequently, existing language technologies fall short on accuracy, scalability and portability.
The goal of this project is to deliver innovative, portable open-source real-time methods for cross-lingual mining and summarisation of large-scale stream media. TrendMiner will achieve this through an inter-disciplinary approach, combining deep linguistic methods from text processing, knowledge-based reasoning from web science, machine learning, economics, and political science. No expensive human annotated data will be required due to our use of time-series data (e.g. financial markets, political polls) as a proxy. The project’s results will be generic with many business applications: business intelligence, customer relations management, community support.
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SOLUTIONS
- Developing a scalable, affordable cloud-based infrastructure for real-time text mining from stream media
- Designing weakly supervised machine learning algorithms for automatic discovery of new trends and correlations.
- Validating results in two high-profile case studies: financial decision support and political analysis and monitoring
- Enabling enhanced access to government data archives, summation of online health information, and tracking of hot societal issues
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LINK: http://www.trendminer-project.eu
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