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We report results of a comparison of the accuracy of crowdworkers and sevenNatural Language Processing (NLP) toolkits in solving two important NLP tasks,named-entity recognition (NER) and entity-level sentiment (ELS) analysis. Wehere focus on a challenging dataset, 1,000 political tweets that were collectedduring the U.S. presidential primary election in February 2016. Each tweetrefers to at least one of four presidential candidates, i.e., four namedentities. The groundtruth, established by experts in political communication,has entity-level sentiment information for each candidate mentioned in thetweet. We tested several commercial and open-source tools. Our experiments showthat, for our dataset of political tweets, the most accurate NER system, GoogleCloud NL, performed almost on par with crowdworkers, but the most accurate ELSanalysis system, TensiStrength, did not match the accuracy of crowdworkers by alarge margin of more than 30 percent points.

Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets