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