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Opinions about the 2016 U.S. Presidential Candidates have been expressed inmillions of tweets that are challenging to analyze automatically. Crowdsourcingthe analysis of political tweets effectively is also difficult, due to largeinter-rater disagreements when sarcasm is involved. Each tweet is typicallyanalyzed by a fixed number of workers and majority voting. We here propose acrowdsourcing framework that instead uses a dynamic allocation of the number ofworkers. We explore two dynamic-allocation methods: (1) The number of workersqueried to label a tweet is computed offline based on the predicted difficultyof discerning the sentiment of a particular tweet. (2) The number of crowdworkers is determined online, during an iterative crowd sourcing process, basedon inter-rater agreements between labels.We applied our approach to 1,000twitter messages about the four U.S. presidential candidates Clinton, Cruz,Sanders, and Trump, collected during February 2016. We implemented the twoproposed methods using decision trees that allocate more crowd efforts totweets predicted to be sarcastic. We show that our framework outperforms thetraditional static allocation scheme. It collects opinion labels from the crowdat a much lower cost while maintaining labeling accuracy.

Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election