Dataset

This dataset was created by Alexander Stephens

Released under Database: Open Database, Contents: © Original Authors

Contents

Fake reviews from Amazon

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 generated fake reviews dataset, containing 20k fake reviews and 20k real product reviews. OR = Original reviews (presumably human created and authentic); CG = Computer-generated fake reviews.

Citation

Salminen, J., Kandpal, C., Kamel, A. M., Jung, S., & Jansen, B. J. (2022). Creating and detecting fake reviews of online products. Journal of Retailing and Consumer Services, 64, 102771. https://doi.org/10.1016/j.jretconser.2021.102771

Acknowlegement

Foto von Brett Jordan auf Unsplash

Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset. This dataset mainly includes reviews (ratings, text) and item metadata (desc- riptions, category information, price, brand, and images). Compared to the pre- vious versions, the 2023 version features larger size, newer reviews (up to Sep 2023), richer and cleaner meta data, and finer-grained timestamps (from day to milli-second).

This dataset comprises customer reviews for Amazon, an online retail giant, featuring insights into customer experiences, including ratings, review titles, texts, and metadata. It is valuable for analyzing customer satisfaction, sentiment, and trends.

Column Descriptions:

Reviewer Name: Identifies the reviewer. Profile Link: Links to the reviewer's profile for additional insights. Country: Indicates the reviewer's location. Review Count: Number of reviews by the same user, showing engagement level. Review Date: When the review was posted, useful for time analysis. Rating: Numerical satisfaction measure. Review Title: Summarizes the review sentiment. Review Text: Detailed customer feedback. Date of Experience: When the service/product was experienced.

Prospective applications:

Sentiment Analysis: Analyze review texts and titles to assess overall customer sentiment toward products, enabling the identification of strengths and weaknesses. Customer Satisfaction Tracking: Track and visualize rating trends over time to understand fluctuations in customer satisfaction. Product Improvement: Identify common themes in reviews to highlight areas for product enhancement or development. Market Segmentation: Use country and demographic information to customize marketing strategies and gain insights into regional preferences. Competitor Analysis: Evaluate customer feedback on Amazon products in comparison to competitors to determine market positioning. Recommendation Systems: Leverage review data to enhance recommendation algorithms, improving personalized shopping experiences. Trend Analysis: Investigate temporal patterns in reviews to link sentiment changes with marketing efforts or product launches.

This extensive dataset serves as a valuable asset for various analyses focused on enhancing customer engagement and refining business strategies.

Context😀 This is a small subset of dataset of headphone reviews from Amazon .

Content: this dataset has 6 columns🎧🎧🎧🎧 1. Customer Name --name of customer who buy the product 2. REVIEW_TITLE-- review in short 3. Color-- color of the product 4. REVIEW_DATE -- date when customer gives rating for eg: 05-Sep-21 5. COMMENTS-- customers comment what are feeling of customer about product 6. RATINGS -- how customer rate out of 5 star for eg: 4/5

Which file to use? There is only one files one is preprocessed ready for sentiment analysis

Acknowledgements This dataset is taken from Amazon product data, https://www.amazon.in/boat-headphones/s?k=boat+headphones

License to the data files belong to them.

Inspiration -Sentiment analysis on reviews. -Understanding how people rate usefulness of a review/ What factors influence helpfulness of a review. -Fake reviews/ outliers.

The dataset contains fake (fraudulent) e-shops data together with legitimate e-shops data. The dataset is balanced and contains 1140 records of 579 fake (fraudulent) and 561 real (legitimate) online shops. Each record contains the following fields: 1. Online shop’s URL; 2. Label - {legitimate, fraudulent}; 3. Domain length - Number of symbols in the host domain name; 4. Top domain length - Number of symbols in the top domain name; 5. Presence of prefix “www” in the active URL of the online shop, values {0 - no, 1 - yes}; 6. Number of digits in the URL; 7. Number of letters in the URL; 8. Number of dots (.) in the URL; 9. Number of hyphens (-) in the URL; 10. Presence of credit card payment, values {0 - no, 1 - yes}; 11. Presence of money back payment, including PayPal, Alipay, Apple Pay, Google Pay, Samsung Pay, and Amazon Pay, values {0 - no, 1 - yes}; 12. Presence of cash on delivery payment, values {0 - no, 1 - yes}; 13. Presence of the ability to use cryptocurrencies for payments, values {0 - no, 1 - yes}; 14. Presence of free contact emails, including Gmail, Hotmail, Outlook, Yahoo Mail, Zoho Mail, ProtonMail, iCloud Mail, GMX Mail, AOL Mail, mail.com, Yandex Mail, Mail2World, or Tutanota, values {0 – email address not found, 1 - free email address, 2 - domain email address, 3 – other email address}; 15. Presence of logo URL, values {0 - no, 1 - yes}; 16. SSL certificate issuer name; 17. SSL certificate expire date; 18. SSL certificate issuer organization name; 19. SSL certificate issuer organization ID, values {1 - Cloudflare, Inc., 2 - Let's Encrypt, 3 - Sectigo Limited, 4 - cPanel, Inc., 5 - GoDaddy.com, Inc., 6 - Amazon, 7 - DigiCert, Inc., 8 - GlobalSign nv-sa, 9 - Google Trust Services LLC, 10 - ZeroSSL, 11 - other organization}; 20. Indication of young domain, registered 400 days ago or later, values {0 - ‘old’ domain name, 1 - ‘young’ domain name, 2 - ‘hidden’}; 21. Domain registration date; 22. Presence of TrustPilot reviews, values {0 - no, 1 - yes}; 23. TrustPilot score, values - real number from 0 to 5 or -1 if no reviews are available; 24. Presence of SiteJabber reviews, values {0 - no, 1 - yes}; 25. Presence in the standard Tranco list, values {0 - no, 1 - yes}; 26. Tranco List rank, values - integer number from 1 to 1000000 or -1 if domain is not listed in the Tranco list.