The operating profit of Yelp with headquarters in the United States amounted to 151.04 million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately 189.84 million U.S. dollars. The trend from 2020 to 2024 shows, furthermore, that this increase happened continuously.

Yelp reported 5.12K in Employees for its fiscal year ending in December of 2024. Data for Yelp | YELP - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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 Inc. Common Stock

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

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

This dataset provides a detailed collection of visitor reviews for Universal Studios branches, ideal for understanding customer sentiment and improving service delivery. It aims to assist organisations in managing a high volume of feedback by categorising reviews and determining overall sentiment from individual comments. This process enables companies to gain a clear insight into visitor feedback, which can lead to increased customer loyalty, business growth, enhanced brand value, and improved profitability. The dataset includes over 50,000 reviews collected from visitors across three Universal Studios locations (Florida, Singapore, and Japan), originally posted on the Trip Advisor website.

Columns

• reviewer: The account name of the individual who submitted the review.
• rating: The rating given by the reviewer, on a scale from 1 (indicating dissatisfaction) to 5 (indicating satisfaction).
• written_date: The date when the review was posted.
• title: The headline or title of the review.
• review_text: The main body of the review content provided by the visitor.
• branch: The specific Universal Studios location to which the review pertains.

Distribution

The dataset is typically provided in a CSV file format, structured as tabular data. It contains over 50,000 records. The ratings distribution shows a strong positive bias, with 28,202 reviews between 4.80 and 5.00, and 13,514 reviews between 4.00 and 4.20. Lower ratings include 1,973 reviews between 1.00 and 1.20, and 1,986 reviews between 2.00 and 2.20. The reviews span a wide period from 24 October 2002 to 30 May 2021. Geographically, Universal Studios Florida accounts for 60% of the reviews, Universal Studios Singapore for 31%, and other locations make up 9%.

Usage

This dataset is perfectly suited for developing and implementing a reviews management system. It can be used to determine overall sentiment from individual comments, helping businesses gain a clear understanding of visitor feedback. Applications include identifying specific areas for improvement, enhancing customer loyalty, boosting business performance, elevating brand value, and increasing profitability. It is also highly relevant for Natural Language Processing (NLP) and text analysis projects aimed at extracting insights from unstructured text data.

Coverage

The dataset covers visitor reviews for three major Universal Studios branches: Florida, Singapore, and Japan, reflecting a global scope. The time frame of the reviews extends from 24 October 2002 to 30 May 2021, offering a substantial historical perspective. The data originates from visitor postings on the Trip Advisor website, representing feedback from a diverse group of theme park guests.

License

CC0

Who Can Use It

This dataset is valuable for businesses in the hospitality and entertainment sectors, such as Universal Studios itself, for internal review management and strategic decision-making. Data analysts and scientists can use it for sentiment analysis, customer behaviour studies, and NLP model training. Marketing professionals can leverage the insights to understand customer preferences and refine branding strategies. Researchers focusing on tourism, consumer feedback, or text analytics will also find it a rich resource.

Dataset Name Suggestions

• Universal Studios Guest Reviews
• Trip Advisor Universal Studios Feedback
• Global Theme Park Visitor Reviews
• Universal Studios Customer Ratings
• Entertainment Venue Reviews

Attributes

Original Data Source: Reviews of Universal Studios

The net earnings of Angi Inc fluctuated considerably between 2012 and 2023. In 2023, the Angi Inc. reported a net loss of almost ** million U.S. dollars.