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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 contains five JSON files and the user agreement. More information about those files can be found here.
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()
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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.
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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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).
This dataset contains reviews of local business in 12 metropolitan areas across 4 countries.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is a subset of the Yelp Challenge, it contains all the reviews in the year of 2015
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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_
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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.
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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.
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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.
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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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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.