TY  - CONF
AU  - Brew, Anthony
AU  - Greene, Derek
AU  - Cunningham, Pádraig
A2  - Coelho, Helder
A2  - Studer, Rudi
A2  - Wooldridge, Michael
T1  - Using Crowdsourcing and Active Learning to Track Sentiment in Online Media
T2  - Proceedings of the 19th European Conference on Artificial Intelligence
PB  - IOS Press
CY  - Amsterdam, The Netherlands, The Netherlands
PY  - 2010/
M2  - 
VL  - 215
IS  - 
SP  - 145
EP  - 150
UR  - http://dl.acm.org/citation.cfm?id=1860967.1860997
M3  - 
KW  - sentiment
KW  - crowdsourcing
KW  - active
KW  - datamining
KW  - analysis
KW  - media
KW  - learning
KW  - online
KW  - web
L1  - 
SN  - 978-1-60750-605-8
N1  - 
N1  - 
AB  - 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 Crowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community. 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.
ER  -