Datasets:

ThiagoFNJ
/

esci-llm-aspect-mining

Why was it created? To provide a product-intrinsic, polarity-labeled aspect-annotation layer over the reviews in the Amazon ESCI benchmark, and to serve as a worked reference for calibrated LLM-as-ETL: extraction whose quality and cost are measured before and during a multi-million-record run. It supports aspect-based sentiment analysis, review-aspect retrieval, and research on LLM extraction reliability at scale.

Composition

What do the instances represent? Each row of review_aspects.parquet is one (review, extracted aspect) pair over the us locale of ESCI-S. product_aspects.parquet aggregates these to (product, facet) polarity counts.

Scope and size. US locale only. 447,924 catalog products; 412,739 carry reviews (≤13 each). 9,507,120 review rows (8,850,361 with an aspect; the rest examined with no product-intrinsic aspect, kept as null-facet rows), over 3,950,828 reviews (99.96% of the 3,952,486 in scope). 5,372,779 product–facet pairs over 408,099 products.

What data does each instance contain? Derived facet phrases (English, 1–4-word lowercase noun phrases naming a product dimension), a polarity label, and join keys (asin, review_no, review_md5). No original review text is included.

Is any information missing? By design: (a) evidence — the verbatim quote grounding each aspect — is computed internally but stripped from the release (Amazon text is not redistributed); (b) 1,658 reviews (0.04%) failed extraction (over-length, exceeding the 4096-token context) and are absent; (c) reviews yielding no product-intrinsic aspect appear as null-facet rows, distinguishing "examined, nothing found" from "not processed".

Labels / ground truth. Facets and polarities are model-generated, not human gold, except the 598-review gold set in gold/ (dev-248 / test-350) used for calibration. Held-out facet F1 is 0.604 (semantic θ=0.80, 95% CI [0.566, 0.638]); polarity accuracy 0.9455.

Errors, noise, redundancies. The annotations carry the extractor's measured error rate. The corpus-scale §4.3 star cross-check confirms polarity is sound in aggregate (monotone in stars across all slices) but makes no per-review guarantee — a low-star review can carry a true positive facet ("comfortable, but fell apart").

Collection & preprocessing

Source. Reviews come from ESCI-S, an unofficial January-2023 scrape of Amazon, joined by ASIN to the Amazon ESCI benchmark. This project does not scrape Amazon and does not mirror ESCI-S.

Extraction. One pass per review through google/gemma-4-12B-it (BF16, served by vLLM with guided JSON decoding, temperature 0), prompt gi9 selected by a GEPA-style loop against the gold set. Facets are constrained to product-intrinsic dimensions; delivery/shipping/price/seller are excluded by the prompt. The full engineering record is in docs/review-aspect-extraction.md.

Uses

Suitable for: aspect-based sentiment analysis, aspect-aware product retrieval/reranking, faceted-search prototypes, and studies of LLM extraction quality, cost, and determinism at scale.

Not suitable for: claims about individual reviews (the signal is aggregate); a locale other than US; or as a source of Amazon review text (there is none here).

PII. The released columns are abstracted facet phrases and hashes — no free text, no user identifiers. Facet phrases are product dimensions and carry negligible PII risk. (The stripped evidence column could in principle have contained incidental PII fragments — another reason it is not distributed.)

Distribution & licensing

Derived annotations (parquets, gold set): CC-BY-4.0. The license applies to the facet phrases and polarity labels — the derivative annotation layer, which is the author's contribution. Attribution is required (see the repository CITATION.cff). The license does not, and legally cannot, extend to the underlying review text: that content is the property of Amazon and its users, is not redistributed here, and no rights over it are granted or implied. The reviews were obtained via ESCI-S, an unofficial scrape that this project neither mirrors nor endorses; anyone reconstructing quotes (by joining ESCI-S on asin+review_no) is responsible for their own lawful access to and use of that source text.
Code: MIT.
ESCI benchmark labels: Apache-2.0 (amazon-science/esci-data).

Maintenance

Maintained by the author. Corrections and re-runs (new prompt/model versions) would be published as new dataset versions; the extractor and prompt are pinned by revision so any version is re-derivable. review_md5 lets consumers verify they joined the correct source review.