Structured review of all air quality monitoring studies and data since 1950

Interactive version: https://lookerstudio.google.com/u/4/reporting/3ca416ac-f930-49ea-9dc4-405d6a344093/page/myv4B

rating

App mobile phones reviews structured dataset. This small dataset is ideal for NLP and to test machine learning algorithms.

Get large dataset from our resources.

Extracted from amazon.

Data included only for apple mobile phones.

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spiralworks/openreview-papers-with-reviews

This dataset contains OpenReview papers with structured reviews. Each batch is stored as a separate parquet file.

  Processing Stats:

Total papers processed: 47,105 Total reviews: 513,449 Filtered papers: 8,038 (papers with at least one valid review) Valid reviews: 24,489 (reviews with proper structure: title, score/rating, confidence)

  Usage

from datasets import load_dataset import pandas as pd

Load specific… See the full description on the dataset page: https://huggingface.co/datasets/spiralworks/openreview-papers-with-reviews.

The Google Reviews & Ratings Dataset provides businesses with structured insights into customer sentiment, satisfaction, and trends based on reviews from Google. Unlike broad review datasets, this product is location-specific—businesses provide the locations they want to track, and we retrieve as much historical data as possible, with daily updates moving forward.

This dataset enables businesses to monitor brand reputation, analyze consumer feedback, and enhance decision-making with real-world insights. For deeper analysis, optional AI-driven sentiment analysis and review summaries are available on a weekly, monthly, or yearly basis.

Dataset Highlights

• Location-Specific Reviews – Reviews and ratings for the locations you provide.
• Daily Updates – New reviews and rating changes updated automatically.
• Historical Data Access – Retrieve past reviews where available.
• AI Sentiment Analysis (Optional) – Summarized insights by week, month, or year.
• Competitive Benchmarking – Compare performance across selected locations.

Use Cases

• Franchise & Retail Chains – Monitor brand reputation and performance across locations.
• Hospitality & Restaurants – Track guest sentiment and service trends.
• Healthcare & Medical Facilities – Understand patient feedback for specific locations.
• Real Estate & Property Management – Analyze tenant and customer experiences through reviews.
• Market Research & Consumer Insights – Identify trends and analyze feedback patterns across industries.

Data Updates & Delivery

• Update Frequency: Daily
• Data Format: CSV for easy integration
• Delivery: Secure file transfer (SFTP or cloud storage)

Data Fields Include:

• Business Name
• Location Details
• Star Ratings
• Review Text
• Timestamps
• Reviewer Metadata

Optional Add-Ons:

• AI Sentiment Analysis – Aggregate trends by week, month, or year.
• Custom Location Tracking – Tailor the dataset to fit your specific business needs.

Ideal for

• Marketing Teams – Leverage real-world consumer feedback to optimize brand strategy.
• Business Analysts – Use structured review data to track customer sentiment over time.
• Operations & Customer Experience Teams – Identify service issues and opportunities for improvement.
• Competitive Intelligence – Compare locations and benchmark against industry competitors.

Why Choose This Dataset?

• Accurate & Up-to-Date – Daily updates ensure fresh, reliable data.
• Scalable & Customizable – Track only the locations that matter to you.
• Actionable Insights – AI-driven summaries for quick decision-making.
• Easy Integration – Delivered in a structured format for seamless analysis.

By leveraging Google Reviews & Ratings Data, businesses can gain valuable insights into customer sentiment, enhance reputation management, and stay ahead of the competition.

Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.

Unlock valuable insights with our comprehensive TripAdvisor Dataset, designed for businesses, analysts, and researchers to track customer reviews, ratings, and travel trends. This dataset provides structured and reliable data from TripAdvisor to enhance market research, competitive analysis, and customer satisfaction strategies.

Dataset Features

Business Listings: Access detailed information on hotels, restaurants, attractions, and other businesses, including names, locations, categories, and contact details. Customer Reviews & Ratings: Extract user-generated reviews, star ratings, review dates, and sentiment analysis to understand customer experiences and preferences. Pricing & Booking Data: Track pricing trends, availability, and booking options for hotels, flights, and travel services. Location & Geographical Insights: Analyze travel trends by region, city, or country to identify popular destinations and emerging markets.

Customizable Subsets for Specific Needs Our TripAdvisor Dataset is fully customizable, allowing you to filter data based on location, business type, review sentiment, or specific keywords. Whether you need broad coverage for industry analysis or focused data for customer insights, we tailor the dataset to your needs.

Popular Use Cases

Customer Satisfaction & Brand Monitoring: Track customer feedback, analyze sentiment, and improve service offerings based on real user reviews. Market Research & Competitive Analysis: Compare business performance, monitor competitor reviews, and identify industry trends. Travel & Hospitality Insights: Analyze travel patterns, popular destinations, and seasonal trends to optimize marketing strategies. AI & Machine Learning Applications: Use structured review data to train AI models for sentiment analysis, recommendation engines, and predictive analytics. Pricing Strategy & Revenue Optimization: Monitor pricing trends and customer demand to optimize pricing strategies for hotels, restaurants, and travel services.

Whether you're analyzing customer sentiment, tracking travel trends, or optimizing business strategies, our TripAdvisor Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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 dataset contains web-scraped comments and product details from Amazon and Mercado Livre, originally gathered for a personal project. It enables research based on a given input in a 'search query' column, paired with the corresponding product title, product link, and the customer review. The same search queries were performed across both Amazon and Mercado Livre platforms. This resource is particularly valuable for those looking to conduct Natural Language Processing (NLP) studies specifically in Portuguese.

Columns

• Search Query: Represents the input string used to search for products. This column includes entries such as 'smartphone' and 'mouse', with an 'Other' category accounting for the majority of queries. There are 8542 total values in this column.
• Product Title: Provides the title of the product retrieved during the search. There are 909 unique product titles recorded.
• Link: Contains the URL directing to the product page on Amazon or Mercado Livre. This column features 908 unique links.
• Review: Holds the actual customer review or comment associated with the product. There are 7668 unique review entries.

Distribution

The data is structured as a dataset, typically provided in a CSV file format. While specific total row counts are not detailed, the dataset includes unique value counts for its columns: 909 unique product titles, 908 unique links, and 7668 unique reviews. The structure facilitates mapping search queries to specific products and their associated reviews.

Usage

This dataset is ideally suited for various applications, especially for those undertaking NLP studies in the Portuguese language. It can be used for sentiment analysis, text classification, topic modelling, and building language models based on e-commerce review data.

Coverage

The dataset primarily covers product reviews from Amazon and Mercado Livre, with a specific focus on content in Portuguese. While Mercado Livre primarily serves Brazil and other Latin American countries, the dataset's listed region is global. It was listed as of 17th June 2025.

License

CC0

Who Can Use It

This dataset is suitable for: * Data Scientists and Researchers: For developing and testing NLP models specifically for Portuguese language processing. * Academics: For linguistic studies on e-commerce communication and consumer sentiment in Portuguese. * Developers: For building applications that require understanding or generating Portuguese text, particularly in the context of product reviews.

Dataset Name Suggestions

• Amazon & Mercado Livre Portuguese Reviews
• E-commerce Product Review Dataset (Portuguese)
• Portuguese NLP E-commerce Comments
• Avaliações em Português - Amazon e Mercado Livre

Attributes

Original Data Source: Avaliações em Português - Amazon e Mercado Livre

This research presents a framework for assessing the sustainability and disaster-resiliency of infrastructure, building upon the foundational work of previous framework and enhanced through an extensive literature review. The framework incorporates critical parameters identified through scholarly studies to address the multifaceted challenges of infrastructure sustainability and resiliency. Validation was achieved via expert questionnaire to ensure the relevance and applicability in real-world scenarios. A case study focusing on road and bridge infrastructure in Bandung City demonstrates the framework's effectiveness, providing insights into local infrastructure conditions and potential improvements. The findings underscore the importance of integrating sustainability and disaster-resilience assessments in infrastructure planning and execution, contributing to more robust and adaptive urban environments. This research is expected to contribute to the planning of sustainable and disaster-resilient infrastructure development in Indonesia and a reference for government policies in achieving sustainable development goals.

Literature Review Software Market Outlook

The global literature review software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 3.2 billion by 2032, growing at a CAGR of 8.2% during the forecast period. This substantial growth is driven by various factors including the increasing need for efficient data management and the rising trend of academic and corporate research activities. The expansion of digital technologies and the increasing volume of research documentation have also significantly contributed to the growth trajectory of the literature review software market.

One of the primary growth factors for the literature review software market is the increasing demand for efficient data organization and management solutions. With the exponential growth of academic research, the need to manage vast amounts of data in a structured and efficient manner has become paramount. Literature review software provides researchers with tools to systematically review, analyze, and synthesize existing research, significantly enhancing research efficiency and accuracy. Furthermore, the integration of artificial intelligence and machine learning algorithms into these software solutions has improved their functionality, enabling more sophisticated data analysis and literature synthesis.

Another driving force behind the growth of this market is the increasing adoption of digital tools and technologies in academic and corporate research. As the digital transformation continues to sweep across various sectors, the academic and research communities are also embracing digital solutions to streamline their workflows. Literature review software, with its advanced features such as automated referencing, real-time collaboration, and cloud storage, is becoming an indispensable tool for researchers. This shift towards digitalization is expected to continue, further propelling the market's growth.

In addition, the rise in interdisciplinary research activities is also fueling the demand for literature review software. Modern research often involves collaboration across different fields, requiring researchers to review and synthesize literature from diverse disciplines. Literature review software helps in managing this complexity by allowing researchers to categorize and analyze literature from multiple sources, thus facilitating comprehensive and multi-faceted research. The increasing complexity of research projects and the need for comprehensive literature reviews are significant factors driving the market's growth.

The integration of Product Reviews Software into literature review processes is becoming increasingly valuable for researchers and organizations. This software allows users to gather and analyze feedback on various research tools and methodologies, providing insights into their effectiveness and user satisfaction. By leveraging product reviews, researchers can make informed decisions about which software solutions best meet their needs, enhancing the overall quality and efficiency of their literature reviews. The ability to access real-time feedback and ratings from other users also fosters a collaborative environment, where researchers can share experiences and recommendations. As the demand for user-centric research tools grows, the role of Product Reviews Software in shaping the literature review landscape is expected to expand significantly.

Looking at the regional outlook, North America currently holds the largest share of the global literature review software market, driven by the presence of leading academic institutions and a strong emphasis on research and development. Europe follows closely, with substantial investments in research infrastructure and increasing adoption of digital tools in academic research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid expansion of higher education institutions and growing research activities. Latin America and the Middle East & Africa are also emerging markets, with increasing awareness and adoption of literature review software solutions.

Component Analysis

The literature review software market can be segmented by component into software and services. The software segment comprises various tools and platforms designed for literature review, inclu

Company Reviews Ratings Market Outlook

The global market size for company reviews and ratings was valued at approximately $2.8 billion in 2023, and it is projected to grow to $5.6 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.1%. This growth is primarily driven by the increasing reliance on digital platforms for consumer decisions and the rising importance of online reputation management for businesses.

One of the major growth factors for the company reviews and ratings market is the surge in internet penetration and the proliferation of smartphones globally. As more consumers access the internet, the volume of online reviews and ratings is expected to increase, thereby driving market growth. Consumers today heavily rely on peer reviews and ratings before making purchasing decisions. This trend is particularly strong among millennials and Gen Z, who prefer to trust user-generated content over traditional advertising. Consequently, businesses are increasingly recognizing the need to manage their online reputation proactively.

Another significant factor contributing to the market's expansion is the growing importance of social media platforms in influencing customer perceptions. Social media has become a crucial channel for customers to voice their opinions and experiences. Positive reviews and ratings on these platforms can significantly enhance a company's brand image, while negative feedback can have the opposite effect. Therefore, businesses are investing heavily in monitoring and managing their social media presence, which in turn is propelling the demand for review and rating platforms.

The integration of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) into review platforms is also playing a pivotal role in market growth. These technologies enable more sophisticated analysis of customer feedback, allowing businesses to gain deeper insights into consumer sentiment and behavior. For instance, sentiment analysis tools can process large volumes of reviews to identify patterns and trends, providing valuable data that can inform business strategies. The adoption of AI and ML is expected to continue to rise, further boosting the market.

The Peer Review System is becoming increasingly vital in the realm of online reviews and ratings. This system allows for a more structured and reliable method of gathering feedback, as it involves a process where reviews are evaluated by other users before being published. This not only enhances the credibility of the reviews but also ensures that the feedback provided is constructive and useful for both consumers and businesses. By implementing a Peer Review System, platforms can significantly reduce the incidence of fake or biased reviews, thereby maintaining the integrity and trustworthiness of their service. As more businesses recognize the value of authentic feedback, the adoption of peer review mechanisms is expected to rise, contributing to the overall growth of the review and ratings market.

Regionally, North America currently holds the largest market share, owing to the high adoption rate of digital technologies and the presence of numerous review platforms. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digital transformation in countries like China and India, coupled with the increasing number of internet users in these regions. As a result, there is a growing demand for online review and rating platforms, which is likely to drive market growth in Asia Pacific.

Platform Type Analysis

The company reviews and ratings market is segmented into web-based and mobile-based platforms. Web-based platforms currently dominate the market, primarily because they offer a more comprehensive and detailed interface for both reviewers and businesses. These platforms often provide various tools for businesses to manage their reviews, respond to customer feedback, and analyze data. The robust functionality and flexibility of web-based platforms make them the preferred choice for many businesses, particularly larger enterprises that require detailed analytics and reporting capabilities.

However, mobile-based platforms are gaining significant traction, driven by the increasing use of smartphones and the convenience they offer to users. Mobile-based review platforms allow consumers to leave reviews and ratings on-the-go, mak

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

Ensuring data privacy is an essential objective competing with the ever-rising capabilities of machine learning approaches fueled by vast amounts of centralized data. Federated learning addresses this conflict by moving the model to the data and ensuring the data itself does not leave a client's device. However, maintaining privacy impels new challenges concerning algorithm performance or fairness of the algorithm's results that remain uncovered from a sociotechnical perspective. We tackle this research gap by conducting a structured literature review and analyzing 152 articles to develop a taxonomy of federated learning applications with nine dimensions and 24 characteristics. Our taxonomy illustrates how different attributes of federated learning may affect the trade-off between an algorithm's privacy, performance, and fairness. Despite an increasing interest in the technical implementation of federated learning, our work is one of the first to emphasize an information systems perspective on this emerging and promising topic.

The metadata set does not comprise any description or summary. The information has not been provided.

Unlock detailed insights with our Amazon UK Shoes Products Reviews Dataset, an invaluable resource for businesses, researchers, and data analysts. This dataset features comprehensive information, including product names, review texts, star ratings, and customer feedback for a wide range of shoe products available on Amazon UK.

Key Features:

• Extensive Coverage: Includes detailed reviews and ratings for various shoe products, helping you analyze customer preferences and trends.
  
  
• Structured Data: Available in easily accessible formats like product review dataset CSV, making it perfect for integration into your analytical workflows.
  
  
• Actionable Insights: Leverage this dataset for customer sentiment analysis, product optimization, and competitive benchmarking.

Why Choose the Amazon UK Shoes Products Reviews Dataset?

Whether you're delving into customer behavior, conducting market research, or improving product offerings, this dataset empowers you to make informed decisions. By working with a dataset enriched with real-world feedback, you can:

• Understand customer preferences: Dive into detailed reviews to uncover patterns in consumer likes and dislikes.
  
  
• Enhance product offerings: Identify gaps and opportunities in the market to better meet customer demands.
  
  
• Boost competitive analysis: Compare customer feedback across different brands and products.

Additional Datasets Available

Explore related datasets like the Amazon product review dataset, offering insights across various categories and regions. For specific needs, our curated product reviews dataset is tailored to help you gain a granular understanding of niche markets.

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.

Protocol used as part of the structured review, and results of review, used to assess whether interviews were being used carefully, and reported on clearly, in conservation research papers

This dataset captures rich, first-hand consumer experiences across financial services brands and products in the UK. It includes structured review metrics (e.g. satisfaction score, NPS, value for money), natural language reviews, and advanced derived data such as sentiment scoring and thematic tags. This is an ideal resource for consultancies, researchers, and AI/ML teams aiming to analyse financial services experiences, benchmark brands, or build consumer behaviour models.

Data is collected directly from Smart Money People’s independent review platform and updated monthly. It is anonymised, GDPR-compliant, and available as review-level raw data or aggregated monthly summaries.

Variants: Date, demographic, financial sector, product category

🛒 Walmart Product Reviews Dataset (6.7K Records)

This dataset contains 6,700+ structured customer reviews from Walmart.com. Each entry includes product-level metadata along with review details, making it ideal for small-scale machine learning models, sentiment analysis, and ecommerce insights.

  📑 Dataset Fields

Column Description

url Direct product page URL

name Product name/title

sku Product SKU (Stock Keeping Unit)

price Product price (numeric, USD)… See the full description on the dataset page: https://huggingface.co/datasets/crawlfeeds/walmart-reviews-dataset.