Use cases that can be supported with Yelp Reviews

A. Market Research and Analysis: Leverage Yelp data to conduct comprehensive market research and analysis in the restaurant industry. Identify emerging culinary trends, popular cuisines, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your restaurant with competitors on Yelp. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your restaurant's online reputation effectively. Track and analyze customer reviews and ratings on Yelp to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Yelp dataset to analyze pricing strategies and revenue trends in the restaurant sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your restaurant's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Yelp reviews to gauge customer satisfaction and sentiment towards your restaurant. Use this information to improve dining experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your restaurant's website based on popular keywords, cuisines, and dining preferences identified in the Yelp dataset. Optimize your content to improve search engine rankings and attract more potential diners.

G. Personalized Marketing Campaigns: Use Yelp data to segment your target audience based on dining preferences, food habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and repeat business.

H. Investment and Expansion Decisions: Access historical and real-time data on restaurant performance and market dynamics from Yelp. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new culinary ventures.

I. Predictive Analytics: Utilize the Yelp dataset to build predictive models that forecast future trends in the restaurant industry. Anticipate shifts in culinary preferences, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Yelp dataset. These dashboards can help restaurant executives and stakeholders get a quick overview of the restaurant's performance and make data-driven decisions.

Incorporating the Yelp dataset into your business processes will enhance your understanding of the restaurant market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive culinary industry.

Yelp reported $48.81M in Cost of Sales for its fiscal quarter ending in September of 2025. Data for Yelp | YELP - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Price-Earnings-Ratio Time Series for Yelp Inc. Yelp Inc. operates a platform that connects consumers with local businesses in the United States and internationally. Its platform covers various categories, including restaurants, shopping, beauty and fitness, health, and other categories, as well as home, local, auto, professional, pets, events, real estate, and financial services. It provides free and paid advertising products to businesses, which include cost-per-click advertising and multi-location Ad products, RepairPal network, as well as enables businesses to deliver targeted advertising to large and high-intent audience; and business listing page products. The company also offers other services comprising Yelp Guest Manager, a subscription-based suite of front-of-house management tools for restaurants, nightlife, and certain other venues, which include online reservations, a waitlist management solution, as well as through hostless kiosks, and seating and server rotation management tools; Yelp Fusion Insights program that offers business owners local analytics and insights through access to its historical data and other proprietary content; and Yelp Fusion, which offers free access to various basic information through publicly available APIs, and paid access to content and data for consumer-facing enterprise use. In addition, it provides content licensing, consumer-interactive tools, as well as allows third-party data providers to update and manage business listing information on behalf of businesses. Further, the company offers its products directly through its sales force; indirectly through partners; and online through its website and business app, as well as non-advertising partner arrangements. It has partnership with Grubhub for providing consumers with a service to place food orders for pickup and delivery. The company was formerly known as Yelp! Inc. and changed its name to Yelp Inc. in August 2012. Yelp Inc. was incorporated in 2004 and is based in San Francisco, California.

• Don't worry about solving CAPTCHAs, rotating proxies or installing headless browsers
• No need to update scrapers with every minor or major website layout or design change

• Simple pricing, pay per successful result only. Say goodbye to being charged for failed requests.

• Filter results by number of reviews, date or language

• Review data includes meta data about customers such as avatar, location, profile url, etc.

• Get page meta data like product price information, rating distribution, etc.

Yelp stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This dataset is about stocks per day. It has 837 rows and is filtered where the stock is YELP. It features 3 columns: stock, and closing price.

Yelp reported $98.07M in EBITDA for its fiscal quarter ending in September of 2025. Data for Yelp | YELP - Ebitda including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Yelp reported $30.58M in Debt for its fiscal quarter ending in June of 2025. Data for Yelp | YELP - Debt including historical, tables and charts were last updated by Trading Economics this last December in 2025.

OpenWeb Ninja's Product Data API provides Product Data, Product Reviews Data, Product Offers, sourced in real-time from Google Shopping - the largest product listings aggregate on the web, listing products from all publicly available e-commerce sites (Amazon, eBay, Walmart + many others).

The API covers more than 35 billion Product Data Listings, including Product Reviews and Product Offers across the web. The API provides over 40 product data points including prices, rating and reviews insights, product details and specs, typical price ranges, and more.

OpenWeb Ninja's Product Data common use cases: - Price Optimization & Price Comparison - Market Research & Competitive Analysis - Product Research & Trend Analysis - Customer Reviews Analysis

OpenWeb Ninja's Product Data Stats & Capabilities: - 35B+ Product Listings - 40+ data points per job listing - Global aggregate - Search by keyword or GTIN/EAN

This dataset is about stocks per day. It has 168 rows and is filtered where the stock is YELP and the date is after the 2nd of September 2024. It features 3 columns: stock, and opening price.

Yelp reported $1.96B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Yelp | YELP - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

📱 Global Mobile Reviews Dataset (2025 Edition)

🌍 Research-Based, Web-Scraped Global Review Collection

This dataset presents a curated collection of over 50,000 mobile phone reviews gathered through web scraping, market analysis, and content aggregation from multiple e-commerce and tech review platforms.
It covers eight countries and includes detailed user opinions, ratings, sentiment polarity, and pricing data across leading smartphone brands.

Each record captures customer experience holistically — spanning demographics, verified purchase details, multi-aspect ratings, and currency-adjusted pricing — making this dataset a powerful asset for research, NLP, and analytics.

🎯 Ideal For

• 🧠 Sentiment Analysis & NLP Modeling
• 💬 Text Classification & Review Mining
• 💰 Market Research & Pricing Analytics
• 📊 Consumer Behavior Studies
• 🤖 AI Model Training & Data Science Projects

🧩 Key Highlights

• 50,000+ mobile reviews scraped from top global sources
• Reviews across 8 major countries and multiple platforms
• Demographic data (customer name, age, location)
• Verified purchase flags for reliability
• Detailed product-level sub-ratings
• Pricing in both USD and local currencies
• Multilingual data support and country-specific sentiment distribution
• Professionally cleaned and normalized for research applications

📦 Brands Covered

🌐 Countries Represented

🧾 Example Record

📈 Research & Analytical Applications

• Sentiment Mining: Detect sentiment polarity in real-world review text
• Cross-Country Analysis: Compare satisfaction trends by region and currency
• Price–Rating Studies: Explore pricing elasticity and value perception
• Demographic Insights: Link sentiment to user age and verified purchase behavior
• Market Comparison: Understand brand trust and perception across regions

🧪 Data Collection & Research Approach

This dataset was compiled through an extensive research process combining web scraping, content aggregation, and analytical validation from multiple open and public review sources including:

• E-commerce platforms (e.g., Amazon, Flipkart, BestBuy, eBay)
• Tech review forums and discussion threads
• Mobile product feedback portals and blogs

Data was then: - Filtered for quality and consistency
- Mapped with real-world pricing and currency exchange rates
- Manually validated for sentiment balance and linguistic variation

⚠️ Note: All data is collected from publicly available review information and anonymized for research and educational use only.
No private or personally identifiable data was used or retained.

🧩 Research Summary

The dataset provides a multi-dimensional representation of the modern mobile ecosystem — integrating global pricing, sentiment trends, and demographic diversity to aid data scientists, researchers, and AI practitioners in building better understanding of customer perspectives.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Yelp (YELP) Stock Forecast: A Bite of Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Foodborne illness is prevented by inspection and surveillance conducted by health departments across America. Appropriate restaurant behavior is enforced and monitored via public health inspections. However, surveillance coverage provided by state and local health departments is insufficient in preventing the rising number of foodborne illness outbreaks. To address this need for improved surveillance coverage we conducted a supplementary form of public health surveillance using social media data: Yelp.com restaurant reviews in the city of San Francisco. Yelp is a social media site where users post reviews and rate restaurants they have personally visited. Presence of keywords related to health code regulations and foodborne illness symptoms, number of restaurant reviews, number of Yelp stars, and restaurant price range were included in a model predicting a restaurant’s likelihood of health code violation measured by the assigned San Francisco public health code rating. For a list of major health code violations see (S1 Table). We built the predictive model using 71,360 Yelp reviews of restaurants in the San Francisco Bay Area. The predictive model was able to predict health code violations in 78% of the restaurants receiving serious citations in our pilot study of 440 restaurants. Training and validation data sets each pulled data from 220 restaurants in San Francisco. Keyword analysis of free text within Yelp not only improved detection of high-risk restaurants, but it also served to identify specific risk factors related to health code violation. To further validate our model we applied the model generated in our pilot study to Yelp data from 1,542 restaurants in San Francisco. The model achieved 91% sensitivity 74% specificity, area under the receiver operator curve of 98%, and positive predictive value of 29% (given a substandard health code rating prevalence of 10%). When our model was applied to restaurant reviews in New York City we achieved 74% sensitivity, 54% specificity, area under the receiver operator curve of 77%, and positive predictive value of 25% (given a prevalence of 12%). Model accuracy improved when reviews ranked highest by Yelp were utilized. Our results indicate that public health surveillance can be improved by using social media data to identify restaurants at high risk for health code violation. Additionally, using highly ranked Yelp reviews improves predictive power and limits the number of reviews needed to generate prediction. Use of this approach as an adjunct to current risk ranking of restaurants prior to inspection may enhance detection of those restaurants participating in high risk practices that may have gone previously undetected. This model represents a step forward in the integration of social media into meaningful public health interventions.

Yelp reported $323.38M in Operating Expenses for its fiscal quarter ending in September of 2025. Data for Yelp | YELP - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Context

Data was scraped from Yelp The whole scraping script is on my GitHub

Yelp Inc. is an American company that develops the Yelp.com website and the Yelp mobile app, which publish crowd-sourced reviews about businesses. It also operates Yelp Guest Manager, a table reservation service. It is headquartered in San Francisco, California.

Content

The dataset covers over 1200 restaurant information in California. This dataset has 18 fields. The fileds are given below: - Yelp URL - Restaurant Name - Street Address - Zip Code - City - State - Price Range - Phone - Rating - Number of Reviews - Website - Menu Link - Image 1 - Image 2 - Image 3 - Category 1 - Category 2 - Category 3

Acknowledgements

Data is from Yelp.

Yelp reported $52.66M in EBIT for its fiscal quarter ending in September of 2025. Data for Yelp | YELP - Ebit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

The top 25 beauty and personal care products on Amazon are priced on average ***** dollars, according to data from the second quarter of 2024. When looking at the median cost of these items, it is slightly less at ***** U.S. dollars. These top beauty and personal care items have an average of around ****** online reviews and a ***-star rating. Price sensitivity in beauty e-commerce Price plays a crucial role in the success of beauty products on Amazon. The majority of bestselling beauty items fall within the one to ten dollar range, followed closely by products priced between ** and ** U.S. dollars. This pricing strategy aligns well with the average price point of ***** dollars for top beauty products, catering to price-conscious online shoppers. The affordability factor likely contributes to the high volume of reviews these products receive, as more consumers can access and try them. Brand dominance and market trends The beauty landscape on Amazon is dynamic, with brand leadership shifting throughout the year. While L'Oréal Paris led the pack in early 2023 with a ** percent market share, CeraVe emerged as the front-runner by July, generating over ** million U.S. dollars in gross merchandise value. This shift reflects changing consumer preferences and the growing popularity of skincare products, which account for ** percent of the top 25 beauty items sold on the platform. The success of these brands, coupled with Amazon's projected growth in health and beauty sales from ** billion to **** billion U.S. dollars by 2027, underscores the platform's significance in the beauty e-commerce sector.