Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets
This work addresses the performance gap between automated tools and human workers in analyzing political social media data, which is incremental as it benchmarks existing methods on a specific dataset.
The study compared the accuracy of crowdworkers and seven NLP toolkits on named-entity recognition and entity-level sentiment analysis for 1,000 political tweets, finding that the best NER system performed nearly as well as crowdworkers, but the best sentiment analysis system was over 30 percentage points less accurate.
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.