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 Google Reviews dataset is perfect for obtaining comprehensive insights into businesses and their customer feedback globally. Easily filter by location, business type, or reviewer details to extract the precise data you need. The Google Reviews dataset includes key data points such as URL, place ID, place name, country, address, review ID, reviewer name, total reviews and photos by the reviewer, reviewer profile URL, and more. This dataset provides valuable information for sentiment analysis, business comparisons, and customer behavior studies.

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.

What Makes Our Data Unique?

Autoscraping’s Google Places Review Data is a premium resource for organizations seeking in-depth consumer insights from a trusted global platform. What sets our data apart is its sheer volume and quality—spanning over 10 million reviews from Google Places worldwide. Each review includes critical attributes such as ratings, comment titles, comment bodies, and detailed sentiment analysis. This data is meticulously curated to capture the authentic voice of consumers, offering a rich source of information for understanding customer satisfaction, brand perception, and market trends.

Our dataset is unique not only because of its scale but also due to the richness of its metadata. We provide granular details about each review, including the review source, place ID, and post date, allowing for precise temporal and spatial analysis. This level of detail enables users to track changes in consumer sentiment over time, correlate reviews with specific locations, and conduct deep dives into customer feedback across various industries.

Moreover, the dataset is continuously updated to ensure it reflects the most current opinions and trends, making it an invaluable tool for real-time market analysis and competitive intelligence.

How is the Data Generally Sourced?

The data is sourced directly from Google Places, one of the most widely used platforms for business reviews and location-based feedback globally. Our robust web scraping infrastructure is specifically designed to extract every relevant piece of information from Google Places efficiently and accurately. We employ advanced scraping techniques that allow us to capture a wide array of review data across multiple industries and geographic locations.

The scraping process is conducted at regular intervals to ensure that our dataset remains up-to-date with the latest consumer feedback. Each entry undergoes rigorous data validation and cleaning processes to remove duplicates, correct inconsistencies, and enhance data accuracy. This ensures that users receive high-quality, reliable data that can be trusted for critical decision-making.

Primary Use-Cases and Verticals

This Google Places Review Data is a versatile resource with a wide range of applications across various verticals:

Consumer Insights and Market Research: Companies can leverage this data to gain a deeper understanding of consumer opinions and preferences. By analyzing ratings, comments, and sentiment across different locations and industries, businesses can identify emerging trends, discover potential areas for improvement, and better align their products or services with customer needs.

Brand Reputation Management: Organizations can use this data to monitor their brand reputation across multiple locations. The dataset enables users to track customer sentiment over time, identify patterns in feedback, and respond proactively to negative reviews. This helps businesses maintain a positive brand image and enhance customer loyalty.

Competitive Analysis: By analyzing reviews and ratings of competitors, companies can gain valuable insights into their strengths and weaknesses. This data can inform strategic decisions, such as product development, marketing campaigns, and customer engagement strategies.

Location-Based Marketing: Marketers can utilize this data to tailor their campaigns based on regional customer preferences and sentiments. The geolocation aspect of the data allows for precise targeting, ensuring that marketing efforts resonate with local audiences.

Product and Service Improvement: Businesses can use the detailed feedback from Google Places reviews to identify specific areas where their products or services may be falling short. This information can be used to drive improvements and innovations, ultimately enhancing customer satisfaction and business performance.

Real-Time Sentiment Analysis: The continuous update of our dataset makes it ideal for real-time sentiment analysis. Companies can track how customer sentiment evolves in response to new products, services, or market events, allowing them to react quickly and adapt to changing market conditions.

How Does This Data Product Fit into Our Broader Data Offering?

Autoscraping’s Google Places Review Data is a vital component of our comprehensive data offering, which spans various industries and geographies. This dataset complements our broader portfolio of consumer feedback data, which includes reviews from other major platforms, social media sentiment data, and customer satisfaction surveys.

By integrating this Google Places data with other datasets in our portfolio, users can develop a more holistic view of consumer behavior and market dynamics. For example, combining review data with sales data or demographic information can provide deeper insights into how different factors influence customer satisfaction and purchasing decisions.

Our commitment to delivering high-...

According to a survey conducted by in December 2022 in India, 25 percent of Indians referred to Google reviews 50 percent to 75 percent of the times they use the search engine to find out more about a business. Comparatively, less than 10 percent of respondents never referred to Google reviews. In response to increasing complaints about fake online reviews, the Bureau of Indian Standards (BIS) guidelines were brought into effect in November 2022.

In the second quarter of 2022, the Google Play Store saw the staggering amount of 6.37 million new reviews posted by users in the United States on the mobile gaming apps hosted on the platform. In the same period, Android finance apps in the Google Play Store received approximately 1.79 million reviews, while apps in the libraries and demo category recorded only 6,582 new reviews from U.S. users during the latest examined quarter.

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.

This dataset provides comprehensive access to product search results from Google Shopping in real-time. Search and compare products, offers, and reviews across multiple major retailers and sources. Perfect for e-commerce applications, price comparison tools, and product discovery platforms. The dataset is delivered in a JSON format via REST API.

This dataset provides comprehensive local business and point of interest (POI) data from Google Maps in real-time. It includes detailed business information such as addresses, websites, phone numbers, emails, ratings, reviews, business hours, and over 40 additional data points. Perfect for applications requiring local business data (b2b lead generation, b2b marketing), store locators, and business directories. The dataset is delivered in a JSON format via REST API.

No description was included in this Dataset collected from the OSF

According to a survey conducted in December 2022, three percent of Indians highly trusted Google reviews and ratings when researching a business. Comparatively, 49 percent of the respondents showed medium levels of trust in Google reviews and ratings. In response to increasing complaints about fake online reviews, the Bureau of Indian Standards (BIS) guidelines were brought into effect in November 2022.

Harness the power of Serpstat's Google Keyword Data Scraping API to gather valuable insights into keyword metrics. Our API provides real-time access to essential data, including current local search volume, historical search volume for the previous twelve months, CPC, and Google Ads competition for each input keyword.

With our API, you can seamlessly integrate keyword data scraping functionality into your applications or platforms, enabling you to make informed decisions and optimize your SEO and marketing strategies effectively. Gain access to comprehensive data for all locations, empowering you to tailor your keyword research efforts to specific geographic areas and target audiences.

Whether you're conducting keyword analysis, ad campaign optimization, or content planning, Serpstat's Google Keyword Data Scraping API delivers the insights you need to drive success in today's competitive digital landscape. Unlock the power of real-time data and historical search volume to stay ahead of the curve and achieve your business goals.

Merchant API will provide you with all essential data and metrics for conducting comprehensive competitor analysis, price monitoring, and market niche research.

With Google Shopping API you can get:

• Google Shopping Products listed for the specified keyword. The results include product title, description in Google Shopping SERP, product rank, price, reviews, and rating as well as the related domain. • Full detailed Google Shopping Product Specification. You will receive all product attributes and their content from the product specification page. • A list of Google Shopping Sellers of the specified product. The provided data for each seller includes related product base and total price, shipment and purchase details, and special offers. • Google Shopping Sellers Ad URL with all additional parameters set by the seller.

With Amazon API you can get:

• Results from Amazon product listings according to the specified keyword (product name), location, and language parameters. • A list of ASINs (unique product identifiers assigned by Amazon) of all modifications listed for the specified product and information about the product prices based on ASIN • Amazon Choice products

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Business Listings Database is the source of point-of-interest data and can provide you with all the information you need to analyze how specific places are used, what kinds of audiences they attract, and how their visitor profile changes over time.

The full fields description may be found on this page: https://docs.dataforseo.com/v3/databases/business_listings/?bash

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Each app (row) has values for catergory, rating, size, and more.

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

googleplaystore.csv

googleplaystore_user_reviews.csv

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A survey conducted in December 2022 revealed that 45 percent of Indians found Google reviews and ratings to be inaccurate when conducting research on a business. Meanwhile, another 37 percent found these reviews to be positively biased. In response to increasing complaints about fake online reviews, the Bureau of Indian Standards (BIS) guidelines were brought into effect in November 2022.

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.