Dataset Card for YelpReviewFull

  Dataset Summary

The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.

  Supported Tasks and Leaderboards

text-classification, sentiment-classification: The dataset is mainly used for text classification: given the text, predict the sentiment.

  Languages

The reviews were mainly written in english.

  Dataset Structure





  Data Instances

A… See the full description on the dataset page: https://huggingface.co/datasets/Yelp/yelp_review_full.

This dataset is a subset of Yelp's businesses, reviews, and user data. It was originally put together for the Yelp Dataset Challenge which is a chance for students to conduct research or analysis on Yelp's data and share their discoveries. In the most recent dataset you'll find information about businesses across 8 metropolitan areas in the USA and Canada.

This dataset provides comprehensive business information and reviews from Yelp. It includes detailed business data, customer reviews, ratings, and search capabilities for local businesses and restaurants. Perfect for applications requiring local business intelligence and customer feedback analysis. The dataset is delivered in a JSON format via REST API.

Dataset containing millions of reviews on Yelp. In addition it contains business data including location data, attributes, and categories.

Dataset Card for Dataset Name

The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2.

  Dataset Description

The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2… See the full description on the dataset page: https://huggingface.co/datasets/yassiracharki/Yelp_Reviews_for_Binary_Senti_Analysis.

Dataset Card for Dataset Name

The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5. In total there are 650,000 trainig samples and 50,000 testing samples.

  Dataset Description

The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 to 5) and review text. The review texts are… See the full description on the dataset page: https://huggingface.co/datasets/yassiracharki/Yelp_Reviews_for_Sentiment_Analysis_fine_grained_5_classes.

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

Dataset

This dataset was created by Ayush Jain

Contents

Traffic analytics, rankings, and competitive metrics for yelp.com as of June 2025

Context

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

Contents

• Reviews.csv: Pulled from the corresponding SQLite table named Reviews in database.sqlite
  
• database.sqlite: Contains the table 'Reviews'
  
  

Data includes:
- Reviews from Oct 1999 - Oct 2012
- 568,454 reviews
- 256,059 users
- 74,258 products
- 260 users with > 50 reviews

Acknowledgements

See this SQLite query for a quick sample of the dataset.

If you publish articles based on this dataset, please cite the following paper:

• J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013.

This dataset corresponds to the paper "'Authentic and amazing': authenticity as an evaluative category in online consumer restaurant reviews" appearing in Cultural Analytics. This dataset provides the R scripts used for the preparation, analysis as well as the import of data to Sketch Engine, the ID lists of the reviews in Corpus 1, 2 and 3, as well as the authenticity lexicons used which were derived from O'Connor et. al (2017) under a CC BY 4.0 license. The IDs correspond the those in the Yelp Dataset at the time of data collection (2019).

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.

Source

Please refer to Yelp for the original JSON file and other datasets. This dataset was created in June 2020 by Yelp. The usage of this dataset should be for academic purposes.

Content

I read the JSON file in Python and convert it to three CSV files:

• covid_features.csv contains all the data (9 features)
• covid_features_banners.csv contains all records that have covid banners, which is good for text analysis and creating graphs like a word cloud
• covid_features_highlights.csv contains all records that have highlights (not FALSE), which you can see it's like a dictionary and contains more data in it.

Acknowledgements

Please read Dataset_User_Agreement.pdf before you proceed with all data files.

Inspiration

It would be interesting to see how virtual services were offered by restaurants during COVID in 2020 and how restaurant businesses strived to communicate and connect with customers on Yelp. There is no numeric data to play with, however, it's still valuable to do some visualizations.

We provide instructions, codes and datasets for replicating the article by Kim, Lee and McCulloch (2024), "A Topic-based Segmentation Model for Identifying Segment-Level Drivers of Star Ratings from Unstructured Text Reviews." This repository provides a user-friendly R package for any researchers or practitioners to apply A Topic-based Segmentation Model with Unstructured Texts (latent class regression with group variable selection) to their datasets. First, we provide a R code to replicate the illustrative simulation study: see file 1. Second, we provide the user-friendly R package with a very simple example code to help apply the model to real-world datasets: see file 2, Package_MixtureRegression_GroupVariableSelection.R and Dendrogram.R. Third, we provide a set of codes and instructions to replicate the empirical studies of customer-level segmentation and restaurant-level segmentation with Yelp reviews data: see files 3-a, 3-b, 4-a, 4-b. Note, due to the dataset terms of use by Yelp and the restriction of data size, we provide the link to download the same Yelp datasets (https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset/versions/6). Fourth, we provided a set of codes and datasets to replicate the empirical study with professor ratings reviews data: see file 5. Please see more details in the description text and comments of each file. [A guide on how to use the code to reproduce each study in the paper] 1. Full codes for replicating Illustrative simulation study.txt -- [see Table 2 and Figure 2 in main text]: This is R source code to replicate the illustrative simulation study. Please run from the beginning to the end in R. In addition to estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships, you will get dendrograms of selected groups of variables in Figure 2. Computing time is approximately 20 to 30 minutes 3-a. Preprocessing raw Yelp Reviews for Customer-level Segmentation.txt: Code for preprocessing the downloaded unstructured Yelp review data and preparing DV and IVs matrix for customer-level segmentation study. 3-b. Instruction for replicating Customer-level Segmentation analysis.txt -- [see Table 10 in main text; Tables F-1, F-2, and F-3 and Figure F-1 in Web Appendix]: Code for replicating customer-level segmentation study with Yelp data. You will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 3 to 4 hours. 4-a. Preprocessing raw Yelp reviews_Restaruant Segmentation (1).txt: R code for preprocessing the downloaded unstructured Yelp data and preparing DV and IVs matrix for restaurant-level segmentation study. 4-b. Instructions for replicating restaurant-level segmentation analysis.txt -- [see Tables 5, 6 and 7 in main text; Tables E-4 and E-5 and Figure H-1 in Web Appendix]: Code for replicating restaurant-level segmentation study with Yelp. you will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 10 to 12 hours. [Guidelines for running Benchmark models in Table 6] Unsupervised Topic model: 'topicmodels' package in R -- after determining the number of topics(e.g., with 'ldatuning' R package), run 'LDA' function in the 'topicmodels'package. Then, compute topic probabilities per restaurant (with 'posterior' function in the package) which can be used as predictors. Then, conduct prediction with regression Hierarchical topic model (HDP): 'gensimr' R package -- 'model_hdp' function for identifying topics in the package (see https://radimrehurek.com/gensim/models/hdpmodel.html or https://gensimr.news-r.org/). Supervised topic model: 'lda' R package -- 'slda.em' function for training and 'slda.predict' for prediction. Aggregate regression: 'lm' default function in R. Latent class regression without variable selection: 'flexmix' function in 'flexmix' R package. Run flexmix with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, conduct prediction of dependent variable per each segment. Latent class regression with variable selection: 'Unconstraind_Bayes_Mixture' function in Kim, Fong and DeSarbo(2012)'s package. Run the Kim et al's model (2012) with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, we can do prediction of dependent variables per each segment. The same R package ('KimFongDeSarbo2012.zip') can be downloaded at: https://sites.google.com/scarletmail.rutgers.edu/r-code-packages/home 5. Instructions for replicating Professor ratings review study.txt -- [see Tables G-1, G-2, G-4 and G-5, and Figures G-1 and H-2 in Web Appendix]: Code to replicate the Professor ratings reviews study. Computing time is approximately 10 hours. [A list of the versions of R, packages, and computer...

IMDB and Yelp are datasets used for sentiment analysis and author identification.

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

The local search engine market, encompassing platforms like Google, Yelp, and others, is a dynamic and rapidly evolving sector. While precise figures for market size and CAGR are unavailable, we can infer significant growth based on the consistently increasing reliance on digital platforms for local business discovery. The market's value is likely in the billions, driven by the expanding use of smartphones, increased e-commerce activity, and a growing need for businesses to connect with hyperlocal customers. Key drivers include the rising adoption of location-based services, improved mobile search capabilities, and the ongoing evolution of search engine algorithms to provide increasingly relevant and personalized results. Trends like voice search optimization, augmented reality (AR) integration in local search, and the continued importance of local SEO are shaping the market landscape. However, restraints exist, primarily in the form of competition among numerous established players and the ongoing challenge of maintaining data accuracy and managing user reviews effectively. Segmentation within the market includes various platform types (e.g., general search engines, dedicated review sites, hyperlocal directories), business sizes (SMBs vs. large enterprises), and service categories (restaurants, healthcare, home services). The competitive landscape is highly concentrated, with established giants like Google, Yelp, and Facebook holding significant market share. Smaller, niche players continue to compete by offering specialized features or focusing on specific geographic areas or industries. Future growth will depend on technological innovation, the ability to adapt to evolving user behavior, and the successful integration of new data sources and technologies such as AI and machine learning for better search and recommendation capabilities. While challenges remain in addressing issues like data biases and ensuring the accuracy of business information, the overall outlook for the local search engine market remains positive, driven by sustained digital adoption and a growing need for efficient, hyperlocal business discovery.

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

Association between regional consumer-based recreational facility review and county health status: a cross-sectional analysis of data generated from the online social platform Yelp and the Behavioral Risk Factor Surveillance System survey

SYNERGY is a free and open dataset on study selection in systematic reviews, comprising 169,288 academic works from 26 systematic reviews. Only 2,834 (1.67%) of the academic works in the binary classified dataset are included in the systematic reviews. This makes the SYNERGY dataset a unique dataset for the development of information retrieval algorithms, especially for sparse labels. Due to the many available variables available per record (i.e. titles, abstracts, authors, references, topics), this dataset is useful for researchers in NLP, machine learning, network analysis, and more. In total, the dataset contains 82,668,134 trainable data points. The easiest way to get the SYNERGY dataset is via the synergy-dataset Python package. See https://github.com/asreview/synergy-dataset for all information.