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.

The American Customer Satisfaction Index (ACSI) score of the e-commerce website of Amazon.com has fluctuated since 2000. In 2025, the customer satisfaction score of the online retailer was 83 out of 100 ASCI points. Popularity contest Amazon is one of the most popular marketplaces worldwide. In April 2024, the U.S. domain for Amazon ranked the most visited e-commerce and shopping website by share of online visits, with around 13 percent. Ebay came in second with roughly three percent of the visit share, and the Japanese site amazon.co.jp came in third with 2.66 percent. In the same month, global online shoppers visited amazon.com around 2.2 billion times. Why Amazon? Amazon.com is the most used e-commerce website in the world, and in the U.S., the website is far ahead of its competitors. With a significant difference in website visitors of almost 45 percent, ebay.com is second to amazon.com. Furthermore, the retail giant Walmart trails behind with an online visit share of roughly six percent. Amazon is used for various reasons by its customers. For example, the online marketplace is ranked as the leading platform for product research in the U.S., surpassing even search engines in popularity. Low shipping costs, fast deliveries, and affordable product prices are the main reasons for shopping on Amazon.

Enhanced Amazon Product Metadata Index

Comprehensive searchable index containing: - Base metadata for all products - Category hierarchy index with aliases - Price range index with multiple granularities - Keyword index with enhanced product terms - Brand index with known brand aliases

Features: - Improved category matching - Better brand recognition - Enhanced keyword generation - Multiple price range granularities - Normalized text descriptions

Use for advanced product search and… See the full description on the dataset page: https://huggingface.co/datasets/chen196473/amazon_product_2020_metadata.

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

Total-Other-Finance-Cost Time Series for Banco da Amazônia S.A. Banco da Amazônia S.A. provides banking products and services in the Amazon region of Brazil. The company offers savings, current, simplified, and salary accounts; credit cards; capitalization products; various personal and business loans; pension plans; investment funds, bank deposit certificates and deposit receipts, and agribusiness letter of credit; insurance products; and direct debit and online financial services. It also provides financing programs for family farming, machines and equipment, rural and biodiversity activities, trucks, green energy, green business and infrastructure, and rural and non-rural investments, as well as science, technology, and innovation activities; development and merchant marine fund; working capital; foreign trade; and legal entity savings, billing, card advance, business check, automatic debit, cellular messaging, and banking domicile services. Banco da Amazônia S.A. was founded in 1942 and is headquartered in Belém, Brazil.

Context

Dataset is a subset of Amazon Review 2018 dataset. Data used in this project includes reviews for category Electronics. These data have been reduced to extract the 5-core, such that each of the remaining users and items have 5 reviews each. Only part of the data was left.

Content

Includes reviews and corresponding ratings. Columns are following:

• overall - rating of the product (1 to 5)
• vote - helpful votes of the review
• reviewText - text of the review
• summary - summary of the review
• reviewTime - time of the review (raw)

Acknowledgements

Original Data

Amazon Review Data (2018)

Source: https://nijianmo.github.io/amazon/index.html
Description: 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).

Original Paper

Justifying recommendations using distantly-labeled reviews and fined-grained aspects
Jianmo Ni, Jiacheng Li, Julian McAuley
Empirical Methods in Natural Language Processing (EMNLP), 2019

Inspiration

Dataset with reviews and coresponding ratings from 1 to 5 can be used for Sentiment Analysis and other NLP tasks.

In 2025, according to the source's ranking, Apple was the most valuable brand in the world. The technology giant had an estimated brand value of approximately 574.5 billion U.S. dollars, followed by Microsoft, which was worth 461.1 billion dollars. The global dominance of technology companies Big tech companies take the lead of the world’s most valuable brands. Although brand valuation differs among sources due to different methodologies, Apple, Google, Microsoft, and Amazon consistently rank at the top in several rankings. Notably, business technology, media & entertainment, and consumer technology account for the leading industries driving the collective value of the 100 most valuable brands worldwide. The importance of brand value Brand value, not to be confused with brand equity, is a term used in the marketing industry to describe the value of a brand. The term is based on the implication that products with a well-known brand name can generate more money than those with a less well-known name. Strong brands enhance business performance primarily through their influence on three key stakeholder groups: customers, employees, and investors. They influence customer choice and create loyalty, attract, retain, and motivate talent, and lower the cost of financing for companies.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.