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An interface consolidates customer feedback from more than 70 review platforms, assigning entries to specified businesses.
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This dataset is a filtered version of https://huggingface.co/datasets/vincha77/filtered_yelp_restaurant_reviews
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Google Store Reviews of Tiktok NONLITE/ FULLVERSION application
Google Store Reviews of Tiktok LITE application
A collection of restaurant reviews was assembled in 2019 through Python-based web scraping focused on Dutch establishments, capturing both visit experiences and feature-related information. It is organized in the DatasetDict format with three splits: 116,693 training records, 14,587 test records, and 14,587 validation records.
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A dataset card entry for restaurant reviews that flags the need for additional information beyond what is currently provided.
Restaurant_Reviews.tsv is a small text corpus of customer restaurant reviews paired with a binary sentiment label. It is published on the Hugging Face Hub by user KarthikaRajagopal under an unstated license.
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The restaurant_reviews dataset is a small text corpus of roughly one thousand customer review snippets paired with binary sentiment labels, published on the Hugging Face Hub under the user account pachequinho. It is distributed in CSV format with an Apache-2.0 license declaration.
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Dataset with 20k+ customer reviews for 5 star hotels sourced from Yelp
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Stock-symbol-linked customer feedback and earnings call texts are processed with natural language processing to derive sentiment indicators, helping assess ESG exposure and brand perception.
The restaurant_reviews dataset, published on the Hugging Face Hub by user yav1327, is a tabular and text collection of restaurant listings paired with user reviews. It is distributed in Parquet format with a single train split of 13,144 rows and an unstated license.
A library of prebuilt scrapers, called Actors, that turn public websites into structured data, maps, reviews, prices, social profiles, with schedulable runs.
A curated collection drawn from five leading dental journals, comprising 95,763 processed rows prepared for machine learning, with bias reviews and source lineage documentation.
for Regression Models
Get a data sample: RampedUp tracks 35K types of Technographics on 38 Million Companies. Our sources are HTML website code, Job Postings, Social Profiles and User Reviews. Updated Monthly. We have a robust category / sub-category / sector schema acknowledging multiple technologies within on company.
Complete financial and investment data for the world's leading SaaS companies
Specs of 470+ electric cars: battery, range, charging, size & performance