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.

@inproceedings{brew2010using, author = {Brew, Anthony and Greene, Derek and Cunningham, Pádraig}, title = {Using Crowdsourcing and Active Learning to Track Sentiment in Online Media}, editor = {Coelho, Helder and Studer, Rudi and Wooldridge, Michael}, booktitle = {Proceedings of the 19th European Conference on Artificial Intelligence}, series = {Frontiers in Artificial Intelligence and Applications}, publisher = {IOS Press}, address = {Amsterdam, The Netherlands, The Netherlands}, year = {2010}, volume = {215}, pages = {145--150}, url = {http://dl.acm.org/citation.cfm?id=1860967.1860997}, isbn = {978-1-60750-605-8}, keywords = {sentiment, crowdsourcing, active, datamining, analysis, media, learning, online, web}, 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 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.} }

%0 = inproceedings %A = Brew, Anthony and Greene, Derek and Cunningham, Pádraig %B = Proceedings of the 19th European Conference on Artificial Intelligence %C = Amsterdam, The Netherlands, The Netherlands %D = 2010 %I = IOS Press %T = Using Crowdsourcing and Active Learning to Track Sentiment in Online Media %U = http://dl.acm.org/citation.cfm?id=1860967.1860997