Using crowdsourcing and active learning to track sentiment in online media

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Title: Using crowdsourcing and active learning to track sentiment in online media
Authors: Brew, Anthony
Greene, Derek
Cunningham, Pádraig
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Date: 16-Aug-2010
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IOS Press
Copyright (published version): 2010 The authors and IOS Press
Keywords: Sentiment analysisMachine learningCrowdsourcingSocial media
Subject LCSH: Mass media criticism--Data processing
Machine learning
Social media
News Web sites
DOI: 10.3233/978-1-60750-606-5-145
Language: en
Status of Item: Peer reviewed
Is 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.
Appears in Collections:Computer Science Research Collection

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