Dataset

Restaurant_Reviews.tsv

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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What this dataset contains

The file holds 1,000 rows in a single training split, formatted as a tab-separated values table. Each record pairs a free-text user Review with a Liked integer flag, making it a classic small-scale sentiment-classification corpus. The publisher is an individual Hugging Face contributor rather than a commercial data vendor.

Structure and fields

The schema is minimal, with just two columns: Review, stored as a string Value field containing the raw review text, and Liked, stored as an int64 Value field acting as the binary sentiment label. The dataset is distributed in CSV/TSV format and is compatible with the datasets, pandas, polars, and mlcroissant libraries. There are no metadata fields such as timestamps, restaurant identifiers, cuisine tags, or geographic markers.

How analysts use it

Given its size and lack of provenance, this corpus is best suited to educational demonstrations of NLP pipelines, sentiment-model prototyping, or quick benchmarking rather than production alternative-data feeds. Investment analysts would not typically treat a 1,000-row, contributor-uploaded review sample as a source of consumer-spending signal. Any sentiment indicator derived from it would need to be scaled with restaurant-level or chain-level data before it could inform a real fund decision.

Documentation & references 1