This dataset comprises a large-scale collection of Amazon Reviews, gathered in 2023 by the McAuley Lab. Specifically, it focuses on the Amazon Fashion category, containing a total of 800K+ user reviews. It serves as a valuable resource for conducting sentiment analysis.

We will categorize rating ranges into ternary classes as follow: • Ratings from 1 to 2: Negative (-1) • Ratings of 3: Neutral (0) • Ratings from 4 to 5: Positive (1)

Total Positive Sentiments: 346924 Total Negative Sentiments: 346924 Total Neutral Sentiments: 173462

This dataset is suitable for binary classification as it already contains a balance between positive and negative sentiments. However, for ternary classification, it's necessary to balance the target values using undersampling or oversampling techniques.

The dataset encompasses the following fields: 1. Rating: 1.0 to 5.0 2. title: title of the user review 3. text: Text body of the user review 4. images: Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively 5. asin: ID of the product 6. parent_asin: Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. Please use parent ID to find product meta 7. user_id: ID of the reviewer 8. timestamp": Time of the review (Unix time) 9. helpful_vote: Helpful votes of the review 10. verified_purchase: User purchase verification 11. target: Labels for text reviews, where Positive (1), Negative (-1), and Neutral (0) represent the related sentiments.

Dataset DOI: https://doi.org/10.48550/arXiv.2403.03952 Cite Article: Hou et al. (2024) proposed a method for bridging language and items for retrieval and recommendation

Dataset

This dataset was created by Victor Lee

Contents

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Dataset

This dataset was created by Aditya Patro

Released under Apache 2.0

Contents

Dataset

This dataset was created by Wathiq Soualhi

Released under CC0: Public Domain

Contents

seniichev/amazon-fashion-2023-full dataset hosted on Hugging Face and contributed by the HF Datasets community

Between ************ and **************, Levi's was the most popular fashion brand on the Amazon site for France. As a multi-brand platform, Amazon.fr appeared highly fragmented regarding brand presence. According to the data, Levi's accounted for more than **** percent of fashion purchases on Amazon France.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Amazon-fashion-house.

Dataset

This dataset was created by Youssef Emad Eldin Mohamed

Released under Apache 2.0

Contents

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

Dataset

This dataset was created by soimmary_lana_banana

Contents

Credit report of Amazon Com Fba-mdw7-fashion Jam contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Amazon Com C O Dchia Fashion Lifes Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

During 2025 edition of Amazon Prime Day, ** percent of online shoppers indicated their intention to purchase fashion items in the United States. Pet supplies followed with ** percent of preferences.

Discover the booming cross-border clothing e-commerce market! Explore its $26.93B (2025) size, projected growth, key players (Amazon, Alibaba, SHEIN), regional trends, and future opportunities. Learn about challenges and strategies for success in this dynamic sector.

These datasets contain 1.48 million question and answer pairs about products from Amazon.

Metadata includes

• question and answer text

• is the question binary (yes/no), and if so does it have a yes/no answer?

• timestamps

• product ID (to reference the review dataset)

Basic Statistics:

• Questions: 1.48 million

• Answers: 4,019,744

• Labeled yes/no questions: 309,419

• Number of unique products with questions: 191,185

Discover the booming online apparel & footwear market! Our analysis reveals a $500 billion market in 2025, projected to reach $1.5 trillion by 2033, driven by e-commerce, AR/VR, and fast shipping. Learn about key players like Amazon, Nike, and ASOS, market trends, and future growth predictions.

Dataset

This dataset was created by Hozana Reis (Lila)

Contents

Amazon.com was the most popular website for digital shoppers in the U.S. to buy fashion products from in 2024. Department store websites followed, with ** percent of online fashion shoppers using these sites.

Credit report of Amazon Tex Knit Fashion Ltd. contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.