academic research
Amazon Review
Labeling for
Boston University
United States
Overview
Overview
Boston University researchers needed reliable labeled Amazon reviews to train an NLP model for consumer-product analysis. Label Your Data provided independently cross-checked annotations, supporting the development of a classifier for a much larger review dataset.
labeled
dimensions
turnaround
by the resulting model
categories
Client
A Boston University research group was exploring automated text analysis for consumer-product studies. The researchers used Amazon customer reviews to investigate product-related feedback and develop a deep-learning classifier for large-scale review analysis.
Challenges
The research team needed a consistently labeled dataset before expanding its analysis to a much larger review corpus. Subjective customer feedback and overlapping product attributes made reliable classification particularly important.
Complex labeling taxonomy covering 7 product dimensions and sentiment polarity
Limited internal capacity to manually annotate 10,000 customer reviews
Consistent training labels required to support a larger deep-learning classifier
Solution
Before production, Label Your Data trained an eight-person annotation team on the researchers’ labeling requirements. Each annotator completed 50 test reviews to confirm category boundaries, including distinctions such as aesthetics versus performance. Full production began after this initial calibration.
The annotation workflow included:
Two independent teams labeling every review for cross-checking
QA-led discrepancy resolution to establish final classifications
Separate team outputs and a reconciled master sheet delivered using Google Sheets
Need Consistent and Accurate Labels
for Complex NLP Datasets?
Results
The annotated dataset provided the foundation for a deep-learning classifier capable of processing the remaining customer reviews automatically.
The collaboration with Label Your Data delivered:
10,000 labeled reviews delivered in four weeks, without delays
Cohen’s Kappa above 0.7 across all categories, demonstrating consistent annotations
Approximately 590,000 additional reviews auto-annotated by the resulting classifier
Reduced manual labeling workload, allowing researchers to focus on model experiments
Why AI Teams Choose Label Your Data
Data Annotation for Complex Environments
Rely on consistent, high-quality output for complex datasets, detailed taxonomies, and edge cases.
Structured Quality from Pilot to Production
Get quality engineered into every step through onboarding, evolving guidelines, QA, and continuous feedback.
Flexible and Scalable Operations
Adjust team capacity, project size, and delivery model as you scale, with no setup fees or long-term lock-ins.
An Integrated Delivery Partner
Align on goals, workflows, and expectations with a team that integrates into your process from day one.
Projects Led by Annotation Experts
Work with former annotators who understand annotation complexity, quality standards, and high-volume delivery.
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Label Your Data is solving the problem of slow and inefficient data labeling by providing a faster, more efficient solution with high-quality results.
Nuo Yuan & Richard Li
Researcher & Industrial Engineering Student
Trusted by ML Professionals
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