Crowd-labeling fashion reviews with quality control

We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts. We focus on the Aspect-Based Sentiment Analysis task. Our methods filter out inaccurate input from crowd workers but we preserve differ- ent worker labeling to capture the inherent high variability of the opinions. We demonstrate the quality of labeled data based on Facebook’s FastText framework as a baseline.