Trustpilot Reviews Dataset – 20K Sample

This dataset contains a curated sample of 20,000 English-language user reviews sourced exclusively from Trustpilot.com. It is a representative subset of our larger collection containing over 1 million Trustpilot reviews across various industries and companies.

  🗂️ Dataset Overview

Source: Trustpilot
Total Records: 20,000
Language: English
Industries: E-commerce, SaaS, Travel, Finance, Education, and more
Use Case: NLP tasks… See the full description on the dataset page: https://huggingface.co/datasets/crawlfeeds/Trustpilot-Reviews-Dataset-20K-Sample.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

Dataset

This dataset was created by Martina Bohunicka

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.

Access comprehensive product feedback: Reviews, ratings, and insights from multiple review platforms worldwide.

ml-hub/flipkart-reviews dataset hosted on Hugging Face and contributed by the HF Datasets community

The size and share of this market is categorized based on Consulting Services (Strategy Consulting, Operational Consulting, Financial Consulting, Marketing Consulting, IT Consulting) and Consumer Insights (Survey Analysis, Focus Groups, Customer Interviews, Data Analytics, Market Segmentation) and Reputation Management (Online Reviews Analysis, Brand Monitoring, Crisis Management, Feedback Collection, Sentiment Analysis) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

The dataset consists of 16,990 code-mixed (English, Roman Urdu, and Urdu) reviews, categorised into three sentiment classes: positive, negative, and neutral.

In 2021, Google's share of online reviews increased to 71 percent, up from 67 percent in 2020, indicating a rise in willingness from consumers to share their experiences and opinions online. Overall, Google is the platform and search engine on which most consumers leave reviews for local businesses.

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Dataset

This dataset was created by Ayush Jain

Contents

Amazon Customer Reviews Dataset

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazon’s iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Content

• marketplace
  • 2 letter country code of the marketplace where the review was written.
• customer_id
  • Random identifier that can be used to aggregate reviews written by a single author.
• review_id - The unique ID of the review.
• product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
• product_parent
  • Random identifier that can be used to aggregate reviews for the same product.
• product_title
  • Title of the product.
• product_category
  • Broad product category that can be used to group reviews
  • (also used to group the dataset into coherent parts).
• star_rating
  • The 1-5 star rating of the review.
• helpful_votes
  • Number of helpful votes.
• total_votes
  • Number of total votes the review received.
• vine - Review was written as part of the Vine program.
• verified_purchase
  • The review is on a verified purchase.
• review_headline
  • The title of the review.
• review_body
  • The review text.
• review_date
  • The date the review was written.

License

By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the Amazon.com Conditions of Use (https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. https://s3.amazonaws.com/amazon-reviews-pds/license.txt

Acknowledgements

Provided by Amazon... https://s3.amazonaws.com/amazon-reviews-pds/readme.html

Inspiration

What kinds of questions can be answered by the amazon us customer dataset?

hugginglearners/amazon-all-categories-best-sellers-reviews dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset full description: https://www.kaggle.com/datasets/lockiultra/yandex-geo-reviews-embeddings Dataset contains index column, 768 embedding columns and rating column. Each row corresponds to an embedding representation of the review text with same index.

18,403 music reviews scraped from Pitchfork, including relevant metadata such as author, review date, record release year, score, and genre, along with those album's audio features pulled from Spotify's API.

Dataset Card for Amazon Reviews 2018

This dataset is a collection of title-review pairs collected from Amazon, as collected in Ni et al.. See Amazon Reviews 2018 for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.

  Dataset Subsets





  pair subset

Columns: "title", "review" Column types: str, str Examples:{ 'title': "It doesn't fit my machine. I can't seem to ...", 'review': "It doesn't fit my… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/amazon-reviews.

Dataset

This dataset was created by Quốc Vinh Dương

Contents

The Feedback Management System (FMS) market is experiencing robust growth, driven by the increasing need for businesses to understand customer preferences and improve their products and services. The market's expansion is fueled by several key factors: the rising adoption of digital channels and omnichannel strategies, the growing importance of customer experience (CX) as a competitive differentiator, and the increasing availability of advanced analytics tools that provide actionable insights from customer feedback. Businesses across various sectors, including e-commerce, retail, and hospitality, are leveraging FMS to gather real-time feedback, analyze sentiment, identify areas for improvement, and personalize customer interactions. The market is characterized by a diverse range of solutions, from basic survey platforms to sophisticated AI-powered systems capable of analyzing unstructured data like reviews and social media posts. This diversity caters to businesses of all sizes and provides a scalable solution to manage customer feedback effectively. We estimate the market size in 2025 to be around $8 billion, based on observed growth in related sectors and considering a reasonable CAGR (let's assume a conservative 15% based on industry trends). The competitive landscape is highly fragmented, with numerous players offering specialized solutions. Major players like Zendesk, Qualtrics, HubSpot, and SurveyMonkey dominate the market with comprehensive platforms. However, smaller, niche players are also making significant inroads, specializing in specific industries or functionalities, such as review management (Bazaarvoice, Trustpilot, Yotpo) or specific feedback channels (AskNicely, Qualaroo). The future growth of the FMS market will be shaped by advancements in AI and machine learning, enabling more sophisticated sentiment analysis and predictive capabilities. The integration of FMS with CRM and other business intelligence tools will also be crucial for unlocking the full potential of customer feedback data and driving business decisions. Potential restraints include data privacy concerns, the complexity of implementing and integrating FMS into existing systems, and the need for skilled personnel to effectively analyze and interpret feedback data. However, the overall outlook for the FMS market remains positive, with significant opportunities for growth in the coming years.

This statistic presents the information that is considered the most helpful in product reviews according to internet users in the United States as of September 2018. According to the findings, 60 percent of respondents stated that information regarding product performance was considered the most helpful when reading reviews, while in comparison 55 percent of respondents reported that purchaser satisfaction was considered to be most useful for them.

Dataset was collected by web scrapping patients reviews, rates, and conditions of use for 21 common medications such as Acetaminophen, Ibuprofen and ...

Website used for scrapping: https://www.webmd.com/drugs/2/index

To see how the data was collected check the notebook here