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.

In 2021, many online shoppers in the United Kingdom (UK) considered what previous buyers had to say about products before purchasing the items themselves. Approximately **** in *** UK consumers stated they would check online reviews before buying from a particular business. Even more shoppers said they often avoid enterprises with a rating lower than four.

In recent years, it has become increasingly important to the consumer to read up on a product, business, or service before spending any money. In 2021, nearly ** percent of online shoppers typically read between *** and *** customer reviews before making a purchasing decision. Less than *** in *** shoppers did not have a habit of reading customer reviews before buying.

This dataset supports the article entitled "Effects of Customer Reviews on Product Sales of Strong Brands: A Qualitative Comparative Analysis."

The /kaggle/input/online-review-csv/online_review.csv file contains customer reviews from Flipkart. It includes the following columns:

review_id: Unique identifier for each review. product_id: Unique identifier for each product. user_id: Unique identifier for each user. rating: Star rating (1 to 5) given by the user. title: Summary of the review. review_text: Detailed feedback from the user. review_date: Date the review was submitted. verified_purchase: Indicates if the purchase was verified (true/false). helpful_votes: Number of users who found the review helpful. reviewer_name: Name or alias of the reviewer. Uses Sentiment Analysis: Understand customer sentiments. Product Improvement: Identify areas for product enhancement. Market Research: Analyze customer preferences. Recommendation Systems: Improve recommendation algorithms. This dataset is ideal for practicing data analysis and machine learning techniques.

This dataset contains 2 sets of data files that was used in studying genderbias in the evaluation and use of consumer online reviews. AmazonData.csv is data extracted from the Amazon site. YelpData.csv is data from the Yelp site.

Introduction

Trustpilot Statistics: Trustpilot is an enormous online review platform that consumers turn to when they are contemplating a purchase in the hopes of finding reviews from fellow consumers. Trustpilot has become closer to being the most trusted name in online reviews in 2024, with millions of reviews written for thousands of businesses across the globe.

Here is an article that deeply investigates all the primary dimensions of Trustpilot statistics for the year 2024, covering user growth, impact on businesses, and performance.

Websites that display reviews from other users encourage shoppers to complete their purchases. According to a 2022 global study, sites selling home appliances and electronics that display ratings and reviews increased conversion rates by ** percent. Likewise, online clothing stores saw conversion rates increase by ** percent. However, the musical instruments niche saw the most striking change. Through impressions from online reviews, conversion rates rose by more than ** percent.

About This Data

This is a list of over 71,045 reviews from 1,000 different products provided by Datafiniti's Product Database. The dataset includes the text and title of the review, the name and manufacturer of the product, reviewer metadata, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to assess how writing quality impacts positive and negative online product reviews. E.g.:

• Do reviewers use punctuation correctly?
• Does the number of spelling errors differ by rating?
• What is the distribution of star ratings across products?
• How does review length differ by rating?
• How long is the typical review?
• What is the frequency of words with spelling errors by rating?
• What is the number of reviews that don’t end sentences with punctuation?
• What is the proportion of reviews with spelling errors?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

Amazon Customer Reviews with Sentiment

Extracting Insights from Product Ratings

By [source]

About this dataset

This dataset contains an expansive collection of Amazon customer reviews ranging from 2013 to 2019 found across various categories of products, such as smartphones, laptops, books, and refrigerators. Each customer has their own unique ID, accompanied by a review header containing the title of their review as well as a detailed description and overall rating given by the customer according to their experience. Moreover, we have included our own sentiment analysis providing an additional layer to these reviews - breaking them down into ratings for positive or negative sentiment. With our invaluable insights into customers thoughts and feelings about different products across various categories over 6 years of reviews - this dataset is valuable resource for anyone interested in discovering trends on Amazon's customer base

More Datasets

For more datasets, click here.

Featured Notebooks

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Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Amazon Review Data Web Scrapping - Amazon Review Data Web Scrapping.csv | Column name | Description | |:------------------|:----------------------------------------------------------------| | Category | The product category of the review. (String) | | Review_Header | The title of the customer review. (String) | | Review_text | The detailed text of the customer review. (String) | | Rating | The customer rating of the product. (Integer) | | Own_Rating | The sentiment analysis rating of the customer review. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

OpenWeb Ninja's Product Data API provides Product Data, Product Reviews Data, Product Offers, sourced in real-time from Google Shopping - the largest product listings aggregate on the web, listing products from all publicly available e-commerce sites (Amazon, eBay, Walmart + many others).

The API covers more than 35 billion Product Data Listings, including Product Reviews and Product Offers across the web. The API provides over 40 product data points including prices, rating and reviews insights, product details and specs, typical price ranges, and more.

OpenWeb Ninja's Product Data common use cases: - Price Optimization & Price Comparison - Market Research & Competitive Analysis - Product Research & Trend Analysis - Customer Reviews Analysis

OpenWeb Ninja's Product Data Stats & Capabilities: - 35B+ Product Listings - 40+ data points per job listing - Global aggregate - Search by keyword or GTIN/EAN

The dataset provides insights into restaurant reviews, including customer opinions, ratings, and details about reviewers and restaurants. Key features include:

Review Details:

review_id: Unique identifier for each review. review_text: Textual feedback provided by customers. rating: Numerical rating (e.g., 1–5). Restaurant Information:

restaurant_name: Name of the restaurant reviewed. restaurant_city: City where the restaurant is located. category: Type or cuisine of the restaurant (e.g., Italian, Fast Food). Reviewer Information:

reviewer_name: Name of the individual leaving the review. reviewer_age: Age of the reviewer (if available). Temporal Information:

review_date: Date when the review was posted. Dataset Highlights: Captures diverse customer feedback across multiple cities and categories. Includes both qualitative (textual reviews) and quantitative (ratings) data. Enables temporal analysis with review dates spanning across various years.

This dataset contains structured consumer reviews, paired with ratings of 1–5 stars. It provides ground truth for building and testing models that analyze, predict, or generate review content — as well as tools to monitor satisfaction at scale.

• Full review text (short and long-form)
• Product or service category
• Platform or brand name (Amazon, eBay, Temu, Flipkart, etc.)
• Region, timestamp, and optional sentiment score

Use this dataset to:

• Train models that predict star ratings based on review content
• Benchmark CX performance across products, brands, and platforms
• Fine-tune LLMs on structured consumer feedback data
• Segment customers by satisfaction level for marketing or support targeting
• Track changes in rating distributions over time, category, or geography

This dataset supports customer experience analysts, product teams, and AI developers working on sentiment modeling, product quality tracking, and customer loyalty prediction — with high-volume, real-world data you can trust.

The more you purchase, the lower the price will be.

Access our Trustpilot Reviews Data in CSV Format, offering a comprehensive collection of customer reviews from Trustpilot.

This dataset includes detailed reviews, ratings, and feedback across various industries and businesses. Available in a convenient CSV format, it is ideal for market research, sentiment analysis, and competitive benchmarking.

Leverage this data to gain insights into customer satisfaction, identify trends, and enhance your business strategies. Whether you're analyzing consumer sentiment or conducting competitive analysis, this dataset provides valuable information to support your needs.

In 2022, almost *** in *** consumers in the United States reported always reading ratings and reviews when they shopped online for clothing. In contrast, only ***** percent of survey respondents reported doing so on an occasional basis, indicating that ratings and reviews are an important purchase criterion for online apparel shoppers.

This dataset features consumer reviews about products and services of leading online marketplaces. It's structured to reveal unfiltered product and service experiences. From delivery issues to satisfaction highlights, it reflects what real customers say in their own words — empowering data-driven feedback systems.

Data includes:

-Free-form review text from buyers about global e-commerce platforms -Tagged themes (shipping, quality, returns, pricing, service interaction) -Platform identifier (e.g., Amazon, eBay, Walmart – when available) -Sentiment classification and user tone patterns -Metadata such as review length, category, and product/service type

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

-Analyze common customer feedback themes by product or category -Train feedback recognition models for product QA or escalation detection -Develop AI tools for review clustering, summarization, or rating prediction -Track sentiment shifts on third-party platforms -Identify pain points affecting buyer trust and product reputation

With millions of records and structured insight fields, this dataset helps companies scale customer understanding and automate product intelligence pipelines across marketplace ecosystems.

This comprehensive dataset offers a rich collection of over 5 million customer reviews for hotels and accommodations listed on Booking.com, specifically sourced from the United States. It provides invaluable insights into guest experiences, preferences, and sentiment across various properties and locations within the USA. This dataset is ideal for market research, sentiment analysis, hospitality trend identification, and building advanced recommendation systems.

Key Features:

• Geographic Focus: Exclusively reviews from properties located in the USA.
• Comprehensive Coverage: Includes a wide range of hotel types and sizes across different states and cities in the US, covering reviews from January 2020 to June 2025.
• Rich Detail: Each record provides detailed review information, allowing for in-depth analysis.
• Structured Format: Clean, organized, and ready for immediate use in various analytical tools and platforms.

Dive into a sample of 1,000+ records to experience the dataset's quality. For full access to this comprehensive data, submit your request at Booking reviews data.

Use Cases:

• Market Research: Gain insights into customer preferences and satisfaction in the US hospitality sector.
• Sentiment Analysis: Analyze the emotional tone of reviews to gauge customer sentiment towards hotels and services.
• Competitor Analysis: Benchmark hotel performance and identify areas for improvement against competitors.
• Trend Identification: Discover emerging trends in hotel amenities, service expectations, and guest behavior in the US.
• Recommendation Systems: Develop and train models to recommend hotels based on user preferences and review data.
• Natural Language Processing (NLP): Create and refine NLP models for text summarization, topic modeling, and opinion mining.
• Academic Research: Support studies on tourism, consumer behavior, and data science applications in hospitality.

• Don't worry about solving CAPTCHAs, rotating proxies or installing headless browsers
• No need to update scrapers with every minor or major website layout or design change

• Simple pricing, pay per successful result only. Say goodbye to being charged for failed requests.

• Filter results by number of reviews, date

• Review data includes meta data about customers such as avatar, location, profile url, etc.

• Get page meta data like product price information, rating distribution, etc.

The Yelp dataset is a subset of businesses, reviews, and user data for use in personal, educational, and academic purposes. It contains 6.9M online reviews for 150k businesses. It also includes more than 200,000 images related to the reviews.

The data consists of multiple sub datasets:

1. Yelp Business data: Contains business data including location data, attributes, and categories.
2. Yelp Review data: Contains full review text data including the user_id that wrote the review and the business_id the review is written for.
3. Yelp User data: User data including the user's friend mapping and all the metadata associated with the user.
4. Yelp Checkin data: Checkins on a business.
5. Yelp Tip data: Tips written by a user on a business. Tips are shorter than reviews and tend to convey quick suggestions.
6. Yelp Photo data: Contains photo data including the caption and classification (one of "food", "drink", "menu", "inside" or "outside").

Available as JSON files, use can use it to teach students about databases, to learn NLP, or for sample production data while you learn how to make mobile apps.

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.