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

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.

yashraizada/yelp-open-dataset-checkin dataset hosted on Hugging Face and contributed by the HF Datasets community

The different data sets of point features related to food that we used for the different tasks in our user study. The data was extracted from the Yelp Open Dataset.

yashraizad/yelp-open-dataset-top-businesses dataset hosted on Hugging Face and contributed by the HF Datasets community

This is the The Yelp Collaborative Knowledge Graph (YCKG) - a transformation of the Yelp Open Dataset into RDF format using Y2KG.

Paper Abstract

The Yelp Open Dataset (YOD) contains data about businesses, reviews, and users from the Yelp website and is available for research purposes. This dataset has been widely used to develop and test Recommender Systems (RS), especially those using Knowledge Graphs (KGs), e.g., integrating taxonomies, product categories, business locations, and social network information. Unfortunately, researchers applied naive or wrong mappings while converting YOD in KGs, consequently obtaining unrealistic results. Among the various issues, the conversion processes usually do not follow state-of-the-art methodologies, fail to properly link to other KGs and reuse existing vocabularies. In this work, we overcome these issues by introducing Y2KG, a utility to convert the Yelp dataset into a KG. Y2KG consists of two components. The first is a dataset including (1) a vocabulary that extends Schema.org with properties to describe the concepts in YOD and (2) mappings between the Yelp entities and Wikidata. The second component is a set of scripts to transform YOD in RDF and obtain the Yelp Collaborative Knowledge Graph (YCKG). The design of Y2KG was driven by 16 core competency questions. YCKG includes 150k businesses and 16.9M reviews from 1.9M distinct real users, resulting in over 244 million triples (with 144 distinct predicates) for about 72 million resources, with an average in-degree and out-degree of 3.3 and 12.2, respectively.

Links

Latest GitHub release: https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph/releases/latest

PURL domain: https://purl.archive.org/domain/yckg

Files

Graph Data Triple Files

One sample file for each of the Yelp domains (Businesses, Users, Reviews, Tips and Checkins), each containing 20 entities.

yelp_schema_mappings.nt.gz containing the mappings from Yelp categories to Schema things.

schema_hierarchy.nt.gz containing the full hierarchy of the mapped Schema things.

yelp_wiki_mappings.nt.gz containing the mappings from Yelp categories to Wikidata entities.

wikidata_location_mappings.nt.gz containing the mappings from Yelp locations to Wikidata entities.

Graph Metadata Triple Files

yelp_categories.ttl contains metadata for all Yelp categories.

yelp_entities.ttl contains metadata regarding the dataset

yelp_vocabulary.ttl contains metadata on the created Yelp vocabulary and properties.

Utility Files

yelp_category_schema_mappings.csv. This file contains the 310 mappings from Yelp categories to Schema types. These mappings have been manually verified to be correct.

yelp_predicate_schema_mappings.csv. This file contains the 14 mappings from Yelp attributes to Schema properties. These mappings are manually found.

ground_truth_yelp_category_schema_mappings.csv. This file contains the ground truth, based on 200 manually verified mappings from Yelp categories to Schema things. The ground truth mappings were used to calculate precision and recall for the semantic mappings.

manually_split_categories.csv. This file contains all Yelp categories containing either a & or /, and their manually split versions. The split versions have been used in the semantic mappings to Schema things.

Dataset

This dataset was created by PrivacyMatters

Released under Database: Open Database, Contents: © Original Authors

Contents

Context

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.

Content

This dataset contains five JSON files and the user agreement. More information about those files can be found here.

Code snippet to read the files

in Python, you can read the JSON files like this (using the json and pandas libraries):

import json
import pandas as pd
data_file = open("yelp_academic_dataset_checkin.json")
data = []
for line in data_file:
 data.append(json.loads(line))
checkin_df = pd.DataFrame(data)
data_file.close()

Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset

The Yelp reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

DESCRIPTION

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.

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 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is " ".

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('yelp_polarity_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

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.

yashraizad/yelp-open-dataset-top-users dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is about stocks per day. It has 3,313 rows and is filtered where the stock is YELP. It features 3 columns: stock, and opening price.

These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

Metadata includes

• reviews

• add-to-shelf, read, review actions

• book attributes: title, isbn

• graph of similar books

Basic Statistics:

• Items: 1,561,465

• Users: 808,749

• Interactions: 225,394,930

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

Current food Inspection dataset published using LIVES data standard.

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

These datasets include Food Establishment Inspections processed from the city’s open data initiative (data.boston.gov), and the connection with data scraped from Yelp (namely, Yelp reviews) scraped by BARI. The data is within the city of Boston. The Food Inspections dataset is released by the Health Division of the Department of Inspectional Services of Boston which ensures that all food establishments in the City of Boston meet relevant sanitary codes and standards. The data scraped from Yelp pages includes information about restaurants in Boston that were reviewed on yelp.com. Thus, the data includes two files: Food.Inspections.Records.csv contains information about food inspections at record level (i.e. each record for each restaurant is included). Food.Inspections.Yelp.Restaurant.csv contains information about food inspections at the restaurant level plus information from Yelp reviews also at the restaurant level.

Content

The Health Department has developed an inspection report and scoring system. After conducting an inspection of the facility, the Health Inspector calculates a score based on the violations observed. Violations can fall into:high risk category: records specific violations that directly relate to the transmission of food borne illnesses, the adulteration of food products and the contamination of food-contact surfaces.moderate risk category: records specific violations that are of a moderate risk to the public health and safety.low risk category: records violations that are low risk or have no immediate risk to the public health and safety.The score card that will be issued by the inspector is maintained at the food establishment and is available to the public in this dataset. San Francisco's LIVES restaurant inspection data leverages the LIVES Flattened Schema (https://goo.gl/c3nNvr), which is based on LIVES version 2.0, cited on Yelp's website (http://www.yelp.com/healthscores).

Context

This is a dataset hosted by the city of San Francisco. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore San Francisco's Data using Kaggle and all of the data sources available through the San Francisco organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Autumn Goodman on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

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

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

This dataset, divided into files by city, contains geotagged digital traces collected from different social media platforms, detailed below.

• Tweets - Cheng et al. [1]

• Gowalla [2]

• Tweets - Lamsal [3]

• YELP[4]

• Tweets - Kejriwal et al. [5]

• Geotagged Tweets [6]

• UrbanActivity, [7]

• Brightkite [8]

• Weeplaces [8]

• Flickr [9]

• Foursquare [10]

Each file is named according to the city to which the digital traces were associated and contains the columns:

• Source: contains the name of the source platform
• Event_date: contains the date associated with the digital trace
• Lat: latitude of the digital trace
• Lng: length of the digital trace

The definition of city/town used is provided by Simplemaps [11], which considers a city/town any inhabited place as determined by U.S. government agencies. The location of cities and their respective centers were obtained from the World Cities Database provided by the same company.

A specific group of these cities was utilized for the research presented in the article submitted to Sensors Journal:

Muñoz-Cancino, R., Rios, S. A., & Graña, M. (2023). Clustering cities over features extracted from multiple virtual sensors measuring micro-level activity patterns allows to discriminate large-scale city characteristics. Sensors, Under Review.

Comprehensive guidelines and the selection criteria can be found in the abovementioned article.

References

[1] Zhiyuan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: A content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM '10, page 759{768, New York, NY, USA, 2010. Association for Computing Machinery.
[2] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, page 1082{1090, New York, NY, USA, 2011. Association for Computing Machinery.
[3] Yunhe Feng and Wenjun Zhou. Is working from home the new norm? an observational study based on a large geo-tagged covid-19 twitter dataset, 2020.
[4] Yelp Inc. Yelp Open Dataset, 2021. Retrieved from https://www.yelp.com/dataset. Accessed October 26, 2021.
[5] Mayank Kejriwal and Sara Melotte. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas, January 2021.
[6] Rabindra Lamsal. Design and analysis of a large-scale covid-19 tweets dataset. Applied Intelligence, 51(5):2790{2804, 2021.
[7] Geraud Le Falher, Aristides Gionis, and Michael Mathioudakis. Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In 9th AAAI Conference on Web and Social Media - ICWSM 2015, Oxford, United Kingdom, May 2015.
[8] Yong Liu, WeiWei, Aixin Sun, and Chunyan Miao. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, page 739{748, New York, NY,USA, 2014. Association for Computing Machinery.
[9] Hatem Mousselly-Sergieh, Daniel Watzinger, Bastian Huber, Mario Doller, Elood Egyed-Zsigmond, and Harald Kosch. World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging. In Proceedings of the 5th ACM Multimedia Systems Conference, MMSys '14, page 47{52, New York, NY, USA, 2014. Association for Computing Machinery.
[10] Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129{142, 2015.
[11] Simple Maps. Basic World Cities Database, 2021. Retrieved from https://simplemaps.com/data/world-cities. Accessed September 3, 2021.