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.

1) Data Introduction • The Samsung Customer Reviews Dataset contains 1,000 customer reviews of various Samsung products, including smartphones, tablets, TVs, and smartwatches. User feedback, ratings, and timestamps are included, which are useful for emotional analysis, customer satisfaction surveys, and product quality assessment.

2) Data Utilization (1) Samsung Customer Reviews Dataset has characteristics that: • This dataset contains structured text and numerical information for each review, including product name, username, rating, review title, review body, and creation date, for detailed analysis by review. (2) Samsung Customer Reviews Dataset can be used to: • Customer Opinion Analysis and Emotional Classification: Review texts and ratings can be used to identify customer positive and negative emotions, major complaints and compliments about Samsung products, and to improve products and develop marketing strategies. • Comparison of satisfaction and trend analysis by product: By analyzing review data by product group and period, market trends such as popular products, changes in customer preferences, and repeatedly mentioned issues can be derived and used for competitor analysis or new product planning.

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.

As of 2019, SMBs had higher average review star ratings across all industries than enterprise companies. During the measured period, SMB companies in the financial services sector had an average review star rating of *** out of 5, whereas enterprise businesses only had a *** star rating.

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.

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)

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.

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.

This statistic shows the attitudes towards consumer reviews and recommendations among consumers in Canada as of March 2017. During the survey, ** percent of the responding Canadians said they were likely to buy a product that is recommended by others.

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.

"This dataset includes consumer-submitted reviews from over 5086 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 Telecommunications 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."

The dataset contains reviews from customers about some of Nike's selling shoes. It can be used for customer review sentiment analysis, which is the task of automatically identifying the sentiment (positive, negative, or neutral) of a customer review. The dataset also includes additional information about the reviews, such as the rating, review date, location, username, and title.

Here is a detailed description of each column:

• Rating: The rating that the customer gave to the shoe.
• Review Date: The date on which the review was written.
• Location: The location of the customer who wrote the review.
• Username: The username of the customer who wrote the review.
• Review: The full text of the customer review.
• Fit Feedback: The customer's feedback on the fit of the shoe.
• Comfort Feedback: The customer's feedback on the comfort of the shoe.
• Recommend Feedback: Whether or not the customer recommends the shoe to others.
• Title: The title of the customer review.
• IsPromoReview: Whether or not the customer was incentivized to write the review (e.g., with a discount or free product).
• Subtitle: The subtitle of the customer review.
• ColorDescription: The colour of the shoe that the customer reviewed.
• FullPrice: The full price of the shoe that the customer reviewed.
• Discounted: Whether or not the shoe was on sale when the customer purchased it.
• EmployeePrice: The price that Nike employees pay for the shoe.
• CurrentPrice: The current price of the shoe.
• IsLaunch: Whether or not the shoe is a new release.
• Pid: The unique identifier of the shoe.
• Label: The label of the shoe, such as "Men's" or "Women's."

This dataset is a valuable resource for businesses wanting to understand customers' feedback on their products. The data can be used to identify areas where the effects can be improved, as well as to develop marketing and sales strategies that are tailored to the needs of the customers.

Here are some specific examples of how the data can be used:

• Identify the most important factors to customers: By analyzing the customer reviews, businesses can identify the factors that are most important to customers when choosing a shoe. For example, if many customers are writing reviews about the comfort of the shoe, then this suggests that comfort is an essential factor for customers. This information can be used to design and market shoes that are more comfortable.
• Understand customer sentiment: By analyzing the sentiment of the customer reviews, businesses can understand how customers feel about their products. This information can be used to identify areas where the effects can be improved, as well as to develop marketing and sales strategies that are tailored to the needs of the customers.
• Identify trends: By analyzing customer reviews over time, businesses can identify trends in customer preferences. For example, if more and more customers are writing reviews about sustainable shoes, then this suggests that sustainability is becoming an increasingly important factor for customers. This information can be used to develop new products that meet the needs of the customers.

Overall, the Nike shoe data is a valuable resource for businesses that want to understand their customers' feedback on their products. The data can be used to improve products, develop marketing and sales strategies, and identify trends in customer preferences.

During a 2024 global survey, approximately ** percent of responding consumers reported telling their friends or family about a bad experience with a brand, while ** percent sent feedback directly to the company. Three years earlier, those shares stood at about ** and ** percent, respectively. Meanwhile, the share of respondents who said they did not tell anyone about the bad experience rose from less than ** percent in 2021 to ** percent in 2024.

Hedonic quality adjustment was first introduced in the Consumer Prices Index (CPI) in 2003 for PCs. Since then the use of hedonics has expanded in UK consumer price statistics to include a further five technology products; digital cameras, laptops, mobile phones, pay as you go phones, smartphones and tablet PCs. This article reviews the use of hedonic quality adjustment in consumer price indices in the UK and internationally. It also details the reasons for changing the method of quality adjustment for pay-as-you-go phones and digital cameras, from hedonic adjustment to class mean imputation, from March 2014 onwards. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Review of hedonic quality adjustment

The previous review in this series introduced the notion of data description and outlined some of the more common summary measures used to describe a dataset. However, a dataset is typically only of interest for the information it provides regarding the population from which it was drawn. The present review focuses on estimation of population values from a sample.

Dataset Card for "Amazon Beauty Reviews"

  Dataset Summary

This dataset consists of reviews of "All Beauty" category from amazon. The data includes all ~700,000 reviews up to 2023. Reviews include product and user information, ratings, and a plain text review.

  Supported Tasks and Leaderboards

This dataset can be used for numerous tasks like sentiment analysis, text classification, and user behavior analysis. It's particularly useful for training models to… See the full description on the dataset page: https://huggingface.co/datasets/jhan21/amazon-beauty-reviews-dataset.

This statistic displays the results of a survey conducted in 2017 about the variation between online reviews and actual products received from e-commerce websites according to consumers in India. During the survey period, ** percent of respondents stated that they did experience variations between online reviews and the actual product they received when purchasing from an e-commerce website.