| # Datasheet — ESCI LLM Aspect Mining |
|
|
| Following the *Datasheets for Datasets* framework (Gebru et al., 2021). |
|
|
| ## Motivation |
|
|
| **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/`](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](https://github.com/shuttie/esci-s), an unofficial |
| January-2023 scrape of Amazon, joined by ASIN to the |
| [Amazon ESCI benchmark](https://github.com/amazon-science/esci-data). 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`](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. |
|
|
