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

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 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.

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 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.

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.

Dataset

This dataset was created by Geet Mukherjee

Released under CC0: Public Domain

Contents

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons 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.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

• 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.

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.

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.

This dataset provides a comprehensive and realistic representation of customer sentiment across multiple online and offline shopping platforms. It contains 25,000 customer feedback records, each including demographic attributes, product categories, purchase channels, ratings, review text, and sentiment classification.

The dataset reflects how customers express their experiences on platforms such as Amazon, Flipkart, Meesho, Facebook Marketplace, Myntra, Ajio, Nykaa, Croma, Boat, Reliance Digital, BigBasket, JioMart, Swiggy Instamart, Zepto, and many others. It captures a wide spectrum of sentiments, from highly satisfied customers praising product quality and delivery speed to dissatisfied users reporting issues such as delayed deliveries, low-quality items, or unsatisfactory support.

Each review is paired with a star rating (1 to 5). Ratings of 4 and 5 are mapped to positive sentiment, 3 to neutral, and 1 and 2 to negative sentiment. Corresponding review text is generated to match the sentiment tone, making the dataset ideal for text and sentiment understanding.

In addition to sentiment and rating, the dataset includes essential service metrics such as response time (in hours), whether the issue was resolved, and whether a formal complaint was registered. This creates a richer ecosystem of customer experience and feedback patterns.

The dataset is suitable for a wide variety of uses, including customer insight studies, retail analytics, sentiment analysis, product review exploration, behavior understanding, or business decision making. Since the dataset is fully synthetic and free from personal identifiers, it is safe for all academic, analytical, and research purposes.

Context

Customer reviews are the most crucial information for a company to understand the perspective of the user about the product or service. The dataset is web scraped from Trustpilot website for Nike online store customer reviews.

Content

Reviewer - reviewer of the product Title Content - header of the review title Star_rating - star rating between 1-5 Rating - poor, excellent, good Date - year.month.date format of published date for review Reviews_posted - reviews posted by the reviewer on the website Location - reviewer location

Acknowledgements

this data is part of Trustpilot reviews website which is web scraped

Inspiration

This is the inspiration for me to learn about web scraping to understand the perspective of a real user of the product

Product Reviews Software Market size was valued at USD 8.7 Billion in 2024 and is projected to reach USD 28.9 Billion by 2031, growing at a CAGR of 14.7 % during the forecasted period 2024 to 2031

Global Product Reviews Software Market Drivers

The market drivers for the Product Reviews Software Market can be influenced by various factors. These may include:

Growing Significance of Customer Feedback: As online shopping and e-commerce have grown in popularity, customers now heavily consider customer feedback when making selections about what to buy. Product reviews software helps companies gain credibility and confidence from prospective customers by efficiently gathering, organizing, and presenting consumer feedback.

Put Customer Experience (CX) first: Creating a satisfying consumer experience is a top concern for companies in all sectors. With the use of product reviews software, businesses can get client feedback in real time, pinpoint areas for development, and quickly resolve issues, all of which increase customer happiness and loyalty.

Impact on Purchase Behavior: Research indicates that most consumers base their decisions on what to buy on product reviews. Negative reviews might discourage potential consumers, while positive ratings can greatly impact purchasing behavior and increase sales. Businesses can employ user-generated content to boost conversion rates and spur revenue growth by utilizing product reviews software.

Benefits of SEO: Product reviews and other user-generated content are essential to search engine optimization (SEO). Product reviews software can raise a business's search engine rankings, increase organic traffic to its website, and improve online exposure by producing new and pertinent content. These actions will eventually increase sales and brand awareness.

Enhanced Product Insights: Software for product reviews offers insightful data on consumer preferences, problems, and product effectiveness. Businesses can enhance their product offerings and marketing strategies by identifying patterns, evaluating the strengths and weaknesses of their products, and making data-driven decisions by assessing review data and sentiment.

Social Proof and Trust Building: Positive product reviews act as social proof of a product's dependability, worth, and quality. This promotes trust in the brand. Businesses may differentiate themselves from rivals, gain the trust of prospective customers, and establish a solid reputation for their brands in the marketplace by displaying real client feedback.

Competitive Advantage: Companies can maintain their competitiveness in today's congested markets by putting product reviews software into place. Businesses can set themselves apart from competitors who might not have as strong of a review management strategy, foster brand loyalty, and differentiate their products by aggressively controlling and promoting user reviews.

Brand Engagement and Community Building: Software for product reviews encourages communication and engagement between companies and their clients. Companies may create a feeling of community around their products and brand, develop brand champions, and forge deep connections with their audience by replying to reviews, answering customer questions, and requesting feedback.

Continuous Improvement: Product reviews offer insightful information for new and improved products. Businesses may better satisfy consumer wants and expectations by identifying areas for improvement, iterating on product features, and continuously evolving their offers by listening to customer input.

These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

Metadata includes

• reviews

• price paid (epinions)

• helpfulness votes (librarything)

• flags (librarything)

Gain valuable customer feedback for specific locations with daily updated Google Reviews & Ratings data. Perfect for businesses looking to analyze sentiment, track performance, and benchmark against competitors.

Abstract: With the e-commerce growth, more people are buying products over the internet. To increase customer satisfaction, merchants provide spaces for product and service reviews. Products with positive reviews attract customers, while products with negative reviews lose customers. Following this idea, some individuals and corporations write fake reviews to promote their products and services or defame their competitors. The difficulty for finding these reviews was in the large amount of information available. One solution is to use data mining techniques and tools, such as the classification function. Exploring this situation, the present work evaluates classification techniques to identify fake reviews about products and services on the Internet. The research also presents a literature systematic review on fake reviews. The research used 8 classification algorithms. The algorithms were trained and tested with a hotels database. The CONCENSO algorithm presented the best result, with 88% in the precision indicator. After the first test, the algorithms classified reviews on another hotels database. To compare the results of this new classification, the Review Skeptic algorithm was used. The SVM and GLMNET algorithms presented the highest convergence with the Review Skeptic algorithm, classifying 83% of reviews with the same result. The research contributes by demonstrating the algorithms ability to understand consumers’ real reviews to products and services on the Internet. Another contribution is to be the pioneer in the investigation of fake reviews in Brazil and in production engineering.

Amazon Product Reviews

18 Years of Customer Ratings and Experiences

By Huggingface Hub [source]

About this dataset

The Amazon Reviews Polarity Dataset discloses eighteen years of customers' ratings and reviews from Amazon.com, offering an unparalleled trove of insight and knowledge. Drawing from the immense pool of over 35 million customer reviews, this dataset presents a broad spectrum of customer opinions on products they have bought or used. This invaluable data is a gold mine for improving products and services as it contains comprehensive information regarding customers' experiences with a product including ratings, titles, and plaintext content. At the same time, this dataset contains both customer-specific data along with product information which encourages deep analytics that could lead to great advances in providing tailored solutions for customers. Has your product been favored by the majority? Are there any aspects that need extra care? Use Amazon Reviews Polarity to gain deeper insights into what your customers want - explore now!

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How to use the dataset

• Analyze customer ratings to identify trends: Take a look at how many customers have rated the same product or service with the same score (e.g., 4 stars). You can use this information to identify what customers like or don’t like about it by examining common sentiment throughout the reviews. Identifying these patterns can help you make decisions on which features of your products or services to emphasize in order to boost sales and satisfaction rates.

2 Review content analysis: Analyzing review content is one of the best ways to gauge customer sentiment toward specific features or aspects of a product/service. Using natural language processing tools such as Word2Vec, Latent Dirichlet Allocation (LDA), or even simple keyword search algorithms can quickly reveal general topics that are discussed in relation to your product/service across multiple reviews - allowing you quickly pinpoint areas that may need improvement for particular items within your lines of business.

3 Track associated scores over time: By tracking customer ratings overtime, you may be able to better understand when there has been an issue with something specific related to your product/service - such as negative response toward a feature that was introduced but didn’t seem popular among customers and was removed shortly after introduction.. This can save time and money by identifying issues before they become widespread concerns with larger sets of consumers who invest their money in using your company's item(s).

4 Visualize sentiment data over time graphs : Utilizing visualizations such as bar graphs can help identify trends across different categories quicker than raw numbers alone; combining both numeric values along with color differences associated between different scores allows you spot anomalies easier - allowing faster resolution times when trying figure out why certain spikes occurred where other stayed stable (or vice-versa) when comparing similar data points through time-series based visualization models

Research Ideas

• Developing a customer sentiment analysis system that can be used to quickly analyze the sentiment of reviews and identify any potential areas of improvement.
• Building a product recommendation service that takes into account the ratings and reviews of customers when recommending similar products they may be interested in purchasing.
• Training a machine learning model to accurately predict customers’ ratings on new products they have not yet tried and leverage this for further product development optimization initiatives

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: train.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------| | label | The sentiment of the review, either positive or negative. (String) | | title | The title of the review. (String) ...

"This dataset includes consumer-submitted reviews from over 421 companies, covering both product- and service-based businesses. It’s built to support CX, AI, and analytics teams seeking structured insight into what real customers say, feel, and expect — across the Transport industry.

Each review includes:

• Authentic customer reviews (text, rating, pros and cons)
• Labeled sentiment and tone (positive, neutral, negative)
• Service context across industries: purchase, delivery, support, return, usage
• Industry and company filters (fully customizable per buyer request)
• Optional metadata: platform, review length, timestamp, geo-location

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

Use this dataset to:

• Track public perception trends across specific brands or verticals
• Segment sentiment insights by industry, region, or company
• Power NLP pipelines that require diverse tone, emotion, and domain specificity
• Build dashboards or LLM prompts grounded in real user language
• Train review summarization, classification, or escalation engines

This dataset offers flexibility for custom delivery-by industry, domain, or company, making it ideal for teams needing scalable consumer voice data tailored to specific strategic goals."

Overview: This dataset offers a unique glimpse into customer feedback from the McDonald's Facebook page in Sri Lanka. Compiled from 134 user reviews, it provides insights into customer experiences and sentiments regarding services and products.

Potential Use: Given its concise nature, the dataset is ideal for small-scale sentiment analysis, basic natural language processing (NLP), and customer feedback trends. It can be used to train beginner-level models in text analysis or to conduct a basic study of customer satisfaction trends in the fast-food industry in Sri Lanka.

Column Descriptors: - date: Timestamp of the review - text: The actual review text - user/id: SHA-256 hashed user ID for anonymity - user/name: SHA-256 hashed username for privacy protection

Ethical Data Collection: The data was ethically scraped using the Apify API, ensuring compliance with user privacy and data collection norms.

Acknowledgements: Special thanks to McDonald's Sri Lanka for maintaining an engaging Facebook page and to platforms like Kaggle for hosting and facilitating data sharing within the data science community. This dataset not only serves as a learning tool but also highlights the importance of customer feedback in service improvement. Additionally, the thumbnail image used is credited to McDonald's official website: Large French Fries in Compostable Packaging.

Each week we send thousands of consumers' complaints about financial products and services to companies for response. Those complaints are published here after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaint narratives are consumers' descriptions of their experiences in their own words. By adding their voice, consumers help improve the financial marketplace. The database generally updates daily.