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Using crowdsourcing and active learning to track sentiment in online media
Date Issued
2010-08-16
Date Available
2010-06-01T14:06:20Z
Abstract
Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IOS Press
Copyright (Published Version)
2010 The authors and IOS Press
Subject – LCSH
Mass media criticism--Data processing
Machine learning
Social media
News Web sites
Web versions
Language
English
Status of Item
Peer reviewed
Part of
Coelho, H., Studer, R., Wooldridge, M. (eds.). ECAI 2010 19th European Conference on Artificial Intelligence : Volume 215, Frontiers in Artificial Intelligence and Applications
Conference Details
6th Conference on the Prestigious Applications of Intelligent Systems (PAIS 2010), a sub-conference of the 19th European Conference on Artificial Intelligence (ECAI 2010), 16-20 August, Lisbon, Portugal.
ISBN
978-1-60750-605-8
This item is made available under a Creative Commons License
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