Data includes reviews of different restaurants on Google Maps. There are 1100 comments in total and pictures of each comment in the data set. The data is labeled according to 4 classes (Taste, Menu, Indoor atmosphere, Outdoor atmosphere) for the artificial intelligence to predict. The dataset has been prepared in a way that can be used in both text processing and image processing fields.

The dataset contains the following columns: business_name, author_name, text, photo, rating, rating_category

IMPORTANT: The rating_category column is related to the photo of the review. If you want to use this dataset for NLP, you need to label it yourself. I will label it for you when I am available.

The dataset provides insights into restaurant reviews, including customer opinions, ratings, and details about reviewers and restaurants. Key features include:

Review Details:

review_id: Unique identifier for each review. review_text: Textual feedback provided by customers. rating: Numerical rating (e.g., 1–5). Restaurant Information:

restaurant_name: Name of the restaurant reviewed. restaurant_city: City where the restaurant is located. category: Type or cuisine of the restaurant (e.g., Italian, Fast Food). Reviewer Information:

reviewer_name: Name of the individual leaving the review. reviewer_age: Age of the reviewer (if available). Temporal Information:

review_date: Date when the review was posted. Dataset Highlights: Captures diverse customer feedback across multiple cities and categories. Includes both qualitative (textual reviews) and quantitative (ratings) data. Enables temporal analysis with review dates spanning across various years.

Description

This dataset contains restaurant reviews from TripAdvisor for five European cities, capturing detailed information on users, restaurants (items), and reviews. It offers a comprehensive view of user experiences, opinions, and restaurant attributes.

Data Structure

User Information

• userId: Unique identifier for each user (hashed).
• name: Display name or username.
• location: User's location (city and country).

Restaurant Information (Items)

• itemId: Unique identifier for each restaurant.
• name: Restaurant name.
• city: City where the restaurant is located.
• priceInterval: Price range.
• url: Link to the restaurant’s TripAdvisor review page.
• rating: Average rating score for the restaurant.
• type: List of cuisine types (e.g., [Spanish, Mediterranean]).

Review Information

• reviewId: Unique identifier for each review.
• userId: Corresponding user who wrote the review.
• itemId: Restaurant associated with the review.
• title: Title of the review summarizing the user’s impression.
• text: Full text of the review describing the user’s experience.
• date: Date when the review was posted.
• rating: Numerical score (typically from 0 to 50, where 50 represents the highest satisfaction).
• language: Language of the review.
• images: List of URLs pointing to images uploaded by the user (if available).
• url: Link to the full review on TripAdvisor.

Code example

import pandas as pd

city = "Barcelona"
# Load restaurants
items = pd.read_pickle(f"{city}/items.pkl")
# Load users
users = pd.read_pickle(f"{city}/users.pkl")
# Load reviews
reviews = pd.read_pickle(f"{city}/reviews.pkl")

This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

The global restaurant guide app market size was valued at USD 3.1 billion in 2025 and is projected to reach USD 9.1 billion by 2033, exhibiting a CAGR of 14.8% during the forecast period. The growing smartphone penetration, increasing internet connectivity, and changing consumer preferences toward online food ordering are driving the market growth. Moreover, the rising popularity of food delivery services and the convenience offered by restaurant guide apps are further fueling market expansion. North America and Europe dominate the market, with the United States and the United Kingdom being major contributors. Asia-Pacific is emerging as a promising region, driven by the rapid growth of the food delivery market in China and India. The key players in the market include World of Mouth, Yelp, TheFork, Zomato, DiningCity, Foursquare, UpMenu, TripAdvisor, OpenTable, Eater, Beli, Foodaholix, Zagat, LocalEats, HappyCow, Eatwith, foodpanda, Groupon, and Dianping. These companies are focusing on expanding their geographic presence, adding new features, and partnering with restaurants to enhance their offerings.

Conversational Search for Restaurant Menus Market Outlook

According to our latest research, the global market size for Conversational Search for Restaurant Menus reached USD 1.19 billion in 2024. The sector is experiencing robust growth, with a recorded CAGR of 18.7% from 2025 through 2033. By the end of 2033, the market is projected to achieve a value of USD 5.17 billion. This impressive expansion is primarily driven by the increasing adoption of AI-powered solutions in the hospitality sector, rising consumer demand for seamless digital ordering experiences, and the growing importance of personalized customer engagement in the food service industry.

The primary growth factor for the Conversational Search for Restaurant Menus Market is the rapid digital transformation occurring within the global restaurant industry. Restaurants and food service providers are increasingly leveraging conversational AI to enhance the customer experience by providing instant, accurate, and personalized menu recommendations through voice and text-based interfaces. As smartphone penetration and internet connectivity improve globally, consumers are becoming more accustomed to interacting with digital assistants and chatbots for daily tasks, including ordering food. This shift in consumer behavior is compelling restaurants to integrate conversational search capabilities, which not only streamline the ordering process but also help establishments gather valuable insights into customer preferences, thereby driving operational efficiency and sales growth.

Another significant driver is the competitive landscape of the restaurant industry, which is pushing businesses to differentiate themselves through innovative technologies. Conversational search solutions allow restaurants to offer a unique, interactive experience that can cater to diverse customer needs, such as dietary restrictions, cuisine preferences, and real-time menu availability. This technology is particularly advantageous for food delivery platforms and quick service restaurants, where speed and accuracy are paramount. Furthermore, the integration of advanced natural language processing (NLP) and machine learning algorithms enables these systems to understand complex queries, handle multiple languages, and continuously improve through user interactions. As a result, both large hospitality chains and small independent restaurants are investing in conversational AI to stay competitive and enhance customer loyalty.

The evolution of cloud computing and the proliferation of Software-as-a-Service (SaaS) models have further accelerated the adoption of conversational search in restaurant menus. Cloud-based deployment offers scalability, cost-effectiveness, and ease of integration with existing restaurant management systems. It also enables real-time updates and centralized control, which are crucial for multi-location chains and food aggregators. Additionally, the availability of tailored services and support from specialized vendors has lowered the entry barriers for small and medium-sized enterprises (SMEs), allowing them to implement conversational search solutions without significant upfront investment. This democratization of technology is expanding the market’s reach and fostering innovation across all segments of the food service industry.

Regionally, North America and Europe are leading the adoption of conversational search solutions for restaurant menus, thanks to their advanced digital infrastructure and high consumer awareness. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid urbanization, a burgeoning middle class, and the explosive growth of online food delivery platforms. Countries such as China, India, and Japan are witnessing a surge in demand for digital ordering and personalized dining experiences, prompting local and international players to invest heavily in conversational AI. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by increasing smartphone usage and government initiatives to promote digital transformation in the hospitality sector.

Component Analysis

The Conversational Search for Restaurant Menus Market is segmented by component into software and services, each playing a critical role in the ecosystem. Software solutions form the backbone of conversational search, incorporating advanced natural language processing (NLP), machine learning, and artificial intelligence algorithms to interpret user queries and deliver relevant menu informa

Synthetic Dataset for Product Descriptions and Ads

The basic process was as follows:

Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items. Split the output into desired format `{"product" : "", "description" : ""} Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.

This data was not cleaned or verified manually.

This statistic illustrates some popular ways to share restaurant reviews among Italian restaurant customers in 2017. According to the study results, over ** percent of respondents stated that they shared restaurant reviews by word of mouth among friends and acquaintances. As for online reviews, almost ** percent of respondents wrote reviews on specific review platforms, while almost ** percent used social media.

Introduction

This dataset is a comprehensive collection of information about the top 240 restaurants recommended in Los Angeles, as listed on Yelp. It aims to provide valuable insights into various aspects of these restaurants, such as customer reviews, star ratings, styles of cuisine, and more. The data was collected using a Python script that leverages web scraping techniques to extract relevant information from Yelp's website. 240 restaurants are identified and their URLs are retrieved.

Only the latest 10 comments/reviews are collected for each restaurant. The objective is to unveil the recent performance of the restaurants rather than that in their lifetime.

Methodology

The data was scraped from Yelp's search results for top recommended restaurants in Los Angeles. The Python script uses the BeautifulSoup library to parse the HTML content and extract the required data.

Summary of Results

The dataset contains a total of 240 entries, each representing a top-recommended restaurant in Los Angeles according to Yelp. Each entry is enriched with various details like star ratings, number of reviews, styles of cuisine, and customer comments, among others.

Column Descriptions

Rank: The ranking number of the restaurant in the list of top-recommended restaurants by Yelp CommentDate: The date when the comment was posted. Date: The date when the data was scraped. RestaurantName: The name of the restaurant. Comment: Customer comments about the restaurant. Address: The physical address of the restaurant. StarRating: The average star rating of the restaurant. NumberOfReviews: The total number of reviews the restaurant has received. Style: The style or type of cuisine the restaurant offers. Price: The price range of the restaurant, usually represented in terms of dollar signs (e.g., $$).

Suggested Usages of the Dataset

Market Research: Understand the competitive landscape of restaurants in Los Angeles. Customer Sentiment Analysis: Analyze customer comments to gauge public sentiment about different restaurants. Trend Analysis: Identify popular styles of cuisine or other trends in the restaurant industry. Price Benchmarking: Compare the price ranges of different restaurants to identify potential market gaps. Location Strategy: Use the address data to strategize the best locations for opening a new restaurant.

By leveraging this dataset, stakeholders can make more informed decisions in various domains, from business strategy to customer engagement.

Comprehensive dataset containing 12,653 verified Indian restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

This statistic shows the affects of consumers writing reviews on sites, such as Yelp and Tripadvisor, on restaurants according to professional chefs as of October 2013. During the survey, 52 percent of the respondents stated that negative reviews are damaging to customer traffic and sales.

BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.

Dataset Statistics Total Reviews: 334,119 Features/Columns: 19

Potential Applications Sentiment Analysis Restaurant Review Classification Customer Satisfaction Analysis Opinion Mining Natural Language Processing Research Food Service Industry Analysis

Menu SEO for Restaurant Websites Market Outlook

According to our latest research, the global menu SEO for restaurant websites market size reached USD 1.42 billion in 2024, driven by the rapid digitization of the food service industry and the growing importance of online visibility for restaurants. The market is projected to expand at a CAGR of 13.8% from 2025 to 2033, reaching a forecasted value of USD 4.11 billion by 2033. This robust growth is primarily fueled by the increasing reliance of restaurants on digital platforms to attract and retain customers, as well as the necessity to optimize online menus for both search engines and user experience in an increasingly competitive landscape.

One of the key growth factors for the menu SEO for restaurant websites market is the ongoing transformation of consumer dining behavior. With more customers turning to online channels to discover, evaluate, and order from restaurants, the digital presence of a restaurant has become as crucial as its physical location. Menu SEO ensures that restaurant menus are easily discoverable on search engines, helping establishments appear in local searches, voice searches, and map listings. This not only increases organic traffic but also enhances the likelihood of converting online visitors into paying customers. The proliferation of food delivery platforms and the rising trend of online ordering have further underscored the importance of having well-optimized, search-friendly menus, compelling restaurants of all sizes to invest in specialized SEO solutions.

Another significant factor propelling market growth is the technological advancements in SEO tools and services tailored specifically for the restaurant industry. Modern menu SEO platforms leverage artificial intelligence, natural language processing, and data analytics to optimize menu content, structure, and metadata for maximum search engine visibility. These solutions also facilitate the integration of user-generated content, such as reviews and ratings, which can further boost search rankings. Additionally, the increasing adoption of mobile-first strategies and voice search optimization has compelled restaurants to refine their digital menus for seamless accessibility and discoverability across all devices. As a result, both software and service providers are witnessing heightened demand from restaurants seeking to future-proof their online presence.

Furthermore, the competitive nature of the restaurant industry, combined with the growing influence of online reputation and customer reviews, has made menu SEO an indispensable part of digital marketing strategies. Restaurants are increasingly recognizing that effective menu SEO not only improves their ranking on search engines but also enhances the user experience by providing accurate, up-to-date, and easily navigable menu information. This, in turn, reduces bounce rates and increases customer engagement. The market is also benefitting from the rising awareness among independent restaurants and small-to-medium enterprises (SMEs) about the cost-effectiveness and ROI of menu SEO, which is enabling them to compete with larger chains and franchises on a level digital playing field.

From a regional perspective, North America currently dominates the menu SEO for restaurant websites market, accounting for the largest share in 2024 due to the high penetration of digital technologies and the presence of a mature restaurant industry. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by the rapid urbanization, increasing internet penetration, and the burgeoning food service sector in countries like China, India, and Southeast Asia. Europe and Latin America are also emerging as lucrative markets, with restaurants in these regions increasingly adopting digital strategies to cater to changing consumer preferences and capitalize on the growing trend of online food ordering. Overall, the global market is poised for substantial expansion, supported by technological innovation, evolving consumer habits, and the universal need for enhanced online visibility in the restaurant industry.

A data tracker monitoring Hong Kong's internet trends shows that, between November 14, 2024 to February 11, 2025, the Michelin-starred restaurants receiving the most internet attention were Bo Innovation, Ming Court Wanchai, and Zuicho, all of which specialize in Asian cuisine. During this period, Bo Innovation appeared in well over *** online posts and comments.

This dataset offers a deep dive into how restaurants evolve across time—tracking customer sentiment, rating trajectories, service quality, and operational shifts.

Each restaurant includes a structured timeline of performance phases (e.g., Opening Hype, Peak Performance, Decline, etc.), with dozens of richly written reviews per phase. Ideal for:

• Sentiment analysis
• Time-series modeling
• Customer experience prediction
• Review summarization
• RAG pipelines and LLM fine-tuning

📎 Generated and transformed using OpenAI GPT-4.1 Nano to simulate realistic customer feedback over extended business cycles. Each review is written to reflect unique personas and narrative depth, mimicking organic review dynamics.

🧬 Provenance

This dataset was synthetically generated and annotated using OpenAI’s GPT-4.1 Nano model, leveraging structured prompts to simulate realistic review patterns based on fictional restaurants and their temporal performance phases.

The dataset consists of:

• A reviews JSON and CSV file containing individual customer reviews over time.
• An owner report JSON file summarizing strengths, weaknesses, trends, and recommendations for each restaurant.

No real customer data was used. This dataset is 100% AI-generated and designed for research and modeling purposes.

In a survey conducted in February 2023 in Japan, ** percent of respondents with experiences in finding information about restaurants on social media network stated that they used Instagram. This was the most common platform to do so, followed by Twitter.

Japanese cuisine has found some favorites among food enthusiasts in Hong Kong. An online data tracker shows that five out of the ten most talked-about non-local food restaurants between November 14, 2024 to February 11, 2025 were ********. The list was topped by Sushiro, with ****** online posts and comments related to it during the measured period. When it comes to online media reports, the third place Haidilao, a prominent Chinese hotpot chain operator, attracted the most attention, with *** news articles published online.

This data set is a list of the number and proportion of different types of restaurant in each electoral county in the United States. It also contains other socio-economic and public health data.

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.

We analyzed 100 New York restaurants on Google Maps to show how reviews affect revenue and ratings, with data-backed steps for quick wins so busy teams act fast.