Taking the Pulse of the Web: Assessing Sentiment on Topics in Online Media

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The task of identifying sentiment trends in the popular media has long been of interest to analysts and pundits. Until recently, this task has required professional annotators to manually inspect individual articles in order to identify their polarity. With the increased availability of large volumes of online news content via syndicated feeds, researchers have begun to examine ways to automate aspects of this process. In this work, we describe a sentiment analysis system that uses crowdsourcing to gather non-expert annotations for economic news articles. By using these annotations in conjunction with a supervised machine learning strategy, we can generalize to label a much larger set of articles, allowing us to effectively track sentiment in different news sources over time.

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