# Crowd-Labeling Fashion Reviews with Quality Control

arXiv: https://arxiv.org/abs/1805.09648 · HTML: https://mlsanity.com/paper/1805.09648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection](https://mlsanity.com/paper/2308.12885/) (2023)

## Abstract (arXiv)

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 different 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.