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 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.

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.

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.

t_product_main t_comment_detail t_comment_image_prediction

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.

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.

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.

A November 2021 survey of online users in the United States found that 81 percent of respondents had used Google as a tool to evaluate local businesses in the past 12 months. Yelp was ranked second with over half of respondents using the review platform for such purpose.

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.

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.

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

• 🚨 Your notebook can be here! 🚨!

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 .

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.

The Booking.com Reviews Dataset is a comprehensive collection of user-generated reviews for hotels, hostels, bed & breakfasts, and other accommodations listed on Booking.com. This dataset provides detailed information on customer reviews, including ratings, review text, review dates, customer demographics, and more. It is a valuable resource for analyzing customer sentiment, service quality, and overall guest experiences across different types of accommodations worldwide.

Key Features:

• Review Data: Includes detailed customer reviews with both positive and negative feedback, providing insights into customer experiences and satisfaction levels.
• Ratings: Features individual ratings for various aspects of the accommodations, such as cleanliness, location, service, value for money, and overall satisfaction.
• Review Dates: Provides the dates of each review, enabling trend analysis over time.
• Accommodation Details: Includes information about the accommodations being reviewed, such as name and location.
• Language Support: Reviews are available in multiple languages, reflecting the diverse user base of Booking.com.

Use Cases:

• Sentiment Analysis: Ideal for businesses and researchers conducting sentiment analysis to understand customer opinions and trends in the hospitality industry.
• Market Research: Useful for market research and competitive analysis, identifying strengths and weaknesses of different accommodation types and regions.
• Machine Learning: Beneficial for developing machine learning models for natural language processing, sentiment classification, and recommendation systems.
• Customer Experience Improvement: Helps hotel managers and owners understand customer feedback to improve services and guest experiences.
• Academic Research: Suitable for academic research in hospitality management, consumer behavior, data science, and artificial intelligence.

Dataset Format:

The dataset is available in CSV format making it easy to use for data analysis, machine learning, and application development.

Access 3 million+ US hotel reviews — submit your request today.

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.

-> If you use Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset please cite: https://dergipark.org.tr/en/pub/cukurovaummfd/issue/28708/310341

@research article { cukurovaummfd310341, journal = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi}, issn = {1019-1011}, eissn = {2564-7520}, address = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi Yayın Kurulu Başkanlığı 01330 ADANA}, publisher = {Cukurova University}, year = {2016}, volume = {31}, pages = {464 - 482}, doi = {10.21605/cukurovaummfd.310341}, title = {Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme}, key = {cite}, author = {Gözükara, Furkan and Özel, Selma Ayşe} }

https://doi.org/10.21605/cukurovaummfd.310341

-> Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset is composed as below: ->-> Top 50 E-commerce sites in Turkey are crawled and their comments are extracted. Then randomly 2000 comments selected and manually labelled by a field expert. ->-> After manual labeling the selected comments is done, 600 negative and 600 positive comments are left. ->-> This dataset contains these comments.

-> English_Movie_Reviews_by_Pang_and_Lee_2004 ->-> Pang, B., Lee, L., 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts, In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | polarity dataset v2.0 - review_polarity.tar.gz

-> English_Movie_Reviews_Sentences_by_Pang_and_Lee_2005 ->-> Pang, B., Lee, L., 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 115-124), Association for Computational Linguistics ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | sentence polarity dataset v1.0 - rt-polaritydata.tar.gz

-> English_Product_Reviews_by_Blitzer_et_al_2007 ->-> Article of the dataset: Blitzer, J., Dredze, M., Pereira, F., 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, In ACL (Vol. 7, pp. 440-447). ->-> Source: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/ | processed_acl.tar.gz

-> Turkish_Movie_Reviews_by_Demirtas_and_Pechenizkiy_2013 ->-> Demirtas, E., Pechenizkiy, M., 2013. Cross-lingual polarity detection with machine translation, In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 9). ACM. ->-> http://www.win.tue.nl/~mpechen/projects/smm/#Datasets Turkish_Movie_Sentiment.zip

-> The dataset files are provided as used in the article. -> Weka files are generated with Raw Frequency of terms rather than used Weighting Schemes

-> The folder Cross_Validation contains 10-fold cross-validation each fold files. -> Inside Cross_Validation folder, each turn of the cross-validation is named as test_X where X is the turn number -> Inside test_X folder * Test_Set_Negative_RAW: Contains raw negative class Test data of that cross-validation turn * Test_Set_Negative_Processed: Contains pre-processed negative class Test data of that cross-validation turn * Test_Set_Positive_RAW: Contains raw positive class Test data of that cross-validation turn * Test_Set_Positive_Processed: Contains pre-processed positive class Test data of that cross-validation turn * Train_Set_Negative_RAW: Contains raw negative class Train data of that cross-validation turn * Train_Set_Negative_Processed: Contains pre-processed negative class Train data of that cross-validation turn * Train_Set_Positive_RAW: Contains raw positive class Train data of that cross-validation turn * Train_Set_Positive_Processed: Contains pre-processed positive class Train data of that cross-validation turn * Train_Set_For_Weka: Contains processed Train set formatted for Weka * Test_Set_For_Weka: Contains processed Test set formatted for Weka

-> The folder Entire_Dataset contains files for Entire Dataset * Negative_Processed: Contains all negative comments processed data * Positive_Processed: Contains all positive comments processed data * Negative_RAW: Contains all negative comments RAW data * Positive_RAW: Contains all positive comments RAW data * Entire_Dataset_WEKA: Contains all documents processed data in WEKA format

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.

Trendyol Product Reviews and Ratings Dataset

This dataset contains product reviews and ratings from trendyol.com, one of Turkey's leading e-commerce platforms. The data was collected using a web scraper, and the scraping code is available on GitHub at https://github.com/Alpsencer68/trendyol_comment_scraper.

Dataset Contents:

• Product Name: The name of the product reviewed.
• Review Rating: The rating given to the product by the reviewer (typically ranging from 1 to 5).
• Review Body: The text content of the review provided by the customer.

Usage:

This dataset can be used for various purposes such as sentiment analysis, natural language processing (NLP) tasks, and machine learning projects related to e-commerce and product review analysis.

Firms' incentives to manufacture biased user reviews impede review usefulness. We examine the differences in reviews for a given hotel between two sites: Expedia.com (only a customer can post a review) and TripAdvisor.com (anyone can post). We argue that the net gains from promotional reviewing are highest for independent hotels with single-unit owners and lowest for branded chain hotels with multi-unit owners. We demonstrate that the hotel neighbors of hotels with a high incentive to fake have more negative reviews on TripAdvisor relative to Expedia; hotels with a high incentive to fake have more positive reviews on TripAdvisor relative to Expedia.