12 datasets found
  1. yelp_review_full

    • huggingface.co
    Updated Mar 6, 2012
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    Yelp (2012). yelp_review_full [Dataset]. https://huggingface.co/datasets/Yelp/yelp_review_full
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 6, 2012
    Dataset authored and provided by
    Yelphttp://yelp.com/
    License

    https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

    Description

    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.

  2. Yelp Dataset

    • kaggle.com
    zip
    Updated Mar 17, 2022
    + more versions
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    Yelp, Inc. (2022). Yelp Dataset [Dataset]. https://www.kaggle.com/yelp-dataset/yelp-dataset
    Explore at:
    zip(4374983563 bytes)Available download formats
    Dataset updated
    Mar 17, 2022
    Dataset provided by
    Yelphttp://yelp.com/
    Authors
    Yelp, Inc.
    Description

    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()
    
    
  3. yelp_dataset

    • kaggle.com
    zip
    Updated Apr 9, 2024
    + more versions
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    Sahil Bajaj (2024). yelp_dataset [Dataset]. https://www.kaggle.com/datasets/sahilnbajaj/yelp-dataset
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    zip(0 bytes)Available download formats
    Dataset updated
    Apr 9, 2024
    Authors
    Sahil Bajaj
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    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.

  4. O

    yelp-polarity

    • opendatalab.com
    zip
    Updated Dec 19, 2023
    + more versions
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    New York University (2023). yelp-polarity [Dataset]. https://opendatalab.com/OpenDataLab/yelp-polarity
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    zipAvailable download formats
    Dataset updated
    Dec 19, 2023
    Dataset provided by
    New York University
    Description

    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-challenge 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).

  5. f

    Yelp Review Polarity

    • figshare.com
    txt
    Updated Nov 13, 2020
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    Luís Fred (2020). Yelp Review Polarity [Dataset]. http://doi.org/10.6084/m9.figshare.13232390.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Nov 13, 2020
    Dataset provided by
    figshare
    Authors
    Luís Fred
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Yelp Review Polarity DatasetVersion 1, Updated 09/09/2015ORIGINThe 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_challengeThe 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).

  6. t

    Yelp 2017 Challenge dataset

    • service.tib.eu
    Updated Dec 2, 2024
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    (2024). Yelp 2017 Challenge dataset [Dataset]. https://service.tib.eu/ldmservice/dataset/yelp-2017-challenge-dataset
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    Dataset updated
    Dec 2, 2024
    Description

    This dataset contains reviews of local business in 12 metropolitan areas across 4 countries.

  7. Yelp 2015

    • figshare.com
    txt
    Updated May 21, 2018
    + more versions
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    Zeping Yu (2018). Yelp 2015 [Dataset]. http://doi.org/10.6084/m9.figshare.6292334.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 21, 2018
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Zeping Yu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is a subset of the Yelp Challenge, it contains all the reviews in the year of 2015

  8. Yelp Reviews Full (YELP) 2015

    • zenodo.org
    bin, txt
    Updated Aug 26, 2021
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    N/A; N/A (2021). Yelp Reviews Full (YELP) 2015 [Dataset]. http://doi.org/10.5281/zenodo.5259139
    Explore at:
    txt, binAvailable download formats
    Dataset updated
    Aug 26, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    N/A; N/A
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Yelp Reviews Full (YELP): It was obtained from the Yelp Data Challenge of 2015, containing about 700K reviews on differentkinds of businesses, including restaurants, shopping, home services, etc.


    The files:
    texts.txt: Document set (text). One per line.
    score.txt: Document class whose index is associated with texts.txt
    split_

  9. L

    Local Search Engine Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 9, 2025
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    Market Report Analytics (2025). Local Search Engine Report [Dataset]. https://www.marketreportanalytics.com/reports/local-search-engine-72441
    Explore at:
    pdf, ppt, docAvailable download formats
    Dataset updated
    Apr 9, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The local search engine market is experiencing robust growth, driven by the increasing reliance on mobile devices and the expanding adoption of location-based services. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the proliferation of smartphones equipped with GPS capabilities enables users to easily search for nearby businesses and services. Secondly, the rising popularity of online reviews and ratings significantly influences consumer decisions, boosting the importance of local search engines in driving customer traffic to businesses. Thirdly, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the accuracy and personalization of search results, providing users with a more relevant and efficient search experience. Furthermore, the increasing adoption of local search optimization (SEO) strategies by businesses underscores the crucial role of local search engines in achieving online visibility and driving sales. However, challenges remain. Competition among established players like Google, Yelp, and Facebook is intense. Furthermore, data privacy concerns and the evolving regulatory landscape around data usage could impact the growth trajectory. Segmentation analysis reveals a significant portion of the market is dominated by business users leveraging platforms for advertising and lead generation. Individual users also form a substantial segment, relying on these platforms for discovering local businesses and services. While business directories and review platforms currently hold significant market share, the increasing integration of mapping services and social discovery platforms points toward an evolving landscape where seamless integration across various platforms will become crucial for success. The Asia-Pacific region, particularly China and India, is expected to be a key growth driver owing to rising internet penetration and increasing smartphone usage.

  10. L

    Local Search Engine Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 9, 2025
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    Market Report Analytics (2025). Local Search Engine Report [Dataset]. https://www.marketreportanalytics.com/reports/local-search-engine-72442
    Explore at:
    doc, ppt, pdfAvailable download formats
    Dataset updated
    Apr 9, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The local search engine market is experiencing robust growth, driven by the increasing reliance on mobile devices, the rise of location-based services, and the expanding adoption of digital marketing strategies by businesses. The market, encompassing diverse platforms like business directories (Yelp, Google My Business), review sites (TripAdvisor, Angie's List), mapping services (Google Maps, Apple Maps), and social discovery platforms (Facebook, Nextdoor), is projected to maintain a significant Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). While precise market sizing data is unavailable, reasonable estimations based on publicly available information regarding individual companies within the space and overall digital advertising trends suggest a 2025 market value in the tens of billions of dollars, potentially reaching hundreds of billions by 2033. Key growth drivers include the increasing sophistication of search algorithms, the integration of artificial intelligence (AI) and machine learning (ML) to personalize search results, and the growing demand for hyperlocal information. However, challenges remain, including the evolving privacy regulations, the increasing competition among existing players, and the need to maintain data accuracy and combat fake reviews. Segmentation by user type (individual vs. business) and platform type allows for nuanced analysis of market dynamics, offering opportunities for targeted marketing strategies. Geographic variations in market penetration and growth are also expected. North America currently holds a significant market share due to high internet penetration and technological advancement. However, rapid growth in regions like Asia Pacific and other emerging markets is anticipated, fueled by increasing smartphone adoption and expanding internet infrastructure. The competitive landscape is highly fragmented, with established tech giants like Google and Facebook competing alongside specialized local search providers. Sustained growth will likely depend on continuous innovation in search technology, strategic partnerships, and effective strategies to address evolving consumer needs and preferences in the ever-changing digital landscape.

  11. L

    Local Search Engine Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 9, 2025
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    Market Report Analytics (2025). Local Search Engine Report [Dataset]. https://www.marketreportanalytics.com/reports/local-search-engine-72438
    Explore at:
    ppt, pdf, docAvailable download formats
    Dataset updated
    Apr 9, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The local search engine market is experiencing robust growth, driven by the increasing reliance on mobile devices and the expanding adoption of location-based services. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% through 2033, reaching approximately $150 billion. This expansion is fueled by several key factors: the rising number of smartphone users globally, the proliferation of location-based apps and services (including ride-sharing, food delivery, and e-commerce), and the increasing sophistication of search algorithms in providing highly localized and personalized results. Businesses are increasingly investing in local SEO strategies to enhance their online visibility and attract customers within their geographic proximity, further contributing to market growth. Segmentation within the market reflects this diverse usage, with significant contributions from individual users seeking local information and businesses employing these platforms for marketing and customer engagement. The competition among established players like Google, Yelp, and Facebook, along with emerging niche players, ensures a dynamic and innovative market landscape. However, the market also faces certain challenges. Data privacy concerns and regulations are increasingly impacting how local search engines collect and utilize user data. The evolving landscape of online advertising and the complexities of managing online reputations also pose challenges for both businesses and users. Furthermore, maintaining accuracy and consistency in local business listings across various platforms remains a significant hurdle. Despite these restraints, the long-term outlook for the local search engine market remains positive, driven by ongoing technological advancements, increasing mobile penetration, and the continued evolution of consumer behavior. The strategic expansion into emerging markets, especially in Asia Pacific and Africa, presents substantial opportunities for growth. The ongoing development and refinement of location-based services and improved user experiences will be crucial to shaping the future of this dynamic sector.

  12. L

    Local Search Engine Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 9, 2025
    Share
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    TwitterTwitter
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    Click to copy link
    Link copied
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    Market Report Analytics (2025). Local Search Engine Report [Dataset]. https://www.marketreportanalytics.com/reports/local-search-engine-72440
    Explore at:
    doc, ppt, pdfAvailable download formats
    Dataset updated
    Apr 9, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The local search engine market is experiencing robust growth, driven by the increasing reliance on mobile devices, the proliferation of location-based services, and the expanding adoption of digital marketing strategies by businesses. The market's substantial size, estimated at $50 billion in 2025, reflects the crucial role local search plays in connecting consumers with nearby businesses. A compound annual growth rate (CAGR) of 15% is projected from 2025 to 2033, indicating a significant expansion opportunity. Key growth drivers include the rising penetration of smartphones and the increasing use of location-based apps like navigation systems and food delivery services. Moreover, businesses are increasingly leveraging local SEO strategies to enhance their online visibility and attract local customers. The segmentation reveals the diverse applications, including business directories, review platforms, and social discovery platforms, which cater to both individual and business users. Leading players like Google, Yelp, and Facebook dominate the landscape, continuously innovating their services to stay ahead of the curve. However, challenges remain, such as maintaining data accuracy and addressing privacy concerns. The competitive landscape is dynamic, with established giants competing against emerging startups. Ongoing innovation in areas like AI-powered search, improved map integration, and personalized recommendations are shaping the future of local search. While North America currently holds a significant market share, growth in Asia-Pacific and other emerging markets is expected to fuel the overall market expansion in the coming years. Further growth opportunities lie in improving the user experience through enhanced search results, personalized recommendations, and seamless integration across various platforms. The potential for further market penetration, particularly in underserved regions, signifies a significant opportunity for existing and new entrants. However, regulatory changes relating to data privacy and competition will continue to impact the trajectory of this rapidly evolving market.

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Yelp (2012). yelp_review_full [Dataset]. https://huggingface.co/datasets/Yelp/yelp_review_full
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yelp_review_full

YelpReviewFull

Yelp/yelp_review_full

Explore at:
58 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 6, 2012
Dataset authored and provided by
Yelphttp://yelp.com/
License

https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

Description

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.

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