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Dataset assetOpen Source CommunitySentiment AnalysisText Classification
fancyzhx/amazon_polarity
The Amazon Review Polarity dataset contains product reviews from Amazon, primarily for text‑classification tasks, especially sentiment classification. Reviews rated 1‑2 are labeled negative, 4‑5 positive, and rating 3 is omitted. The dataset includes 3.6 M training samples and 0.4 M test samples; each record comprises a review title, content, and a label (positive or negative). It was created by Xiang Zhang and is widely used as a benchmark for text‑classification research.
Source
hugging_face
Created
Nov 28, 2025
Updated
Jan 9, 2024
Signals
212 views
Availability
Linked source ready
Overview
Dataset description and usage context
Dataset Overview
Dataset Name
- Name: Amazon Review Polarity
- Alias: AmazonPolarity
Basic Information
- Language: English (en)
- License: Apache‑2.0
- Multilinguality: Monolingual
- Size: 1 M < n < 10 M
- Source Data: Raw data
- Task Category: Text classification
- Task ID: Sentiment classification
Dataset Structure
- Features:
- label: Classification label, 0 = negative, 1 = positive
- title: String, review title
- content: String, review content
- Splits:
- Training set: 3,600,000 samples, total size 1,604,364,432 bytes
- Test set: 400,000 samples, total size 178,176,193 bytes
Usage
- Training / Evaluation Metrics:
- Accuracy
- F1 (macro, micro, weighted)
- Precision (macro, micro, weighted)
- Recall (macro, micro, weighted)
Creation
- Creator: Xiang Zhang (xiang.zhang@nyu.edu)
- Purpose: Benchmark for text classification, used in the paper "Character‑level Convolutional Networks for Text Classification"
License Information
- License: Apache License 2.0
Citation
- McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165‑172. 2013.
- Xiang Zhang, Junbo Zhao, Yann LeCun. "Character‑level Convolutional Networks for Text Classification." Advances in Neural Information Processing Systems 28 (NIPS 2015).
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