IL
ENT-00002958 · Dataset

IMDb Large Movie Review Dataset

IMDb Large Movie Review Dataset is a dataset associated with Stanford AI Lab, classified in LXKeys.world as Language / Training Dataset.

Level V PersistentActiveDocumented
Human record

Overview

IMDb Large Movie Review Dataset is a documented language dataset or benchmark. It is recorded as movie review sentiment dataset, supporting evaluation or training for question answering, reasoning, dialogue, classification, translation or multilingual language understanding.

Timeline

  1. 2011Initial dataset release

    IMDb Large Movie Review Dataset entered the documented public record in 2011. This event is retained at the precision supported by the Entry’s reviewed source history.

Capabilities

Dataset referenceBenchmark or training-data contextTask-level documentationModel evaluation supportSource-based traceability

Known limitations

Dataset coverage, licensing, annotation quality and benchmark relevance depend on the source version and use context.Performance claims should be assessed through models evaluated on the dataset rather than inferred from the dataset alone.
Technical and structured detail

Technical description

Structured LXKeys.world registry record for IMDb Large Movie Review Dataset. Entity type: Dataset; classification: Language / Training Dataset; creator/organization context: Stanford AI Lab. Canonical source anchor: https://ai.stanford.edu/~amaas/data/sentiment/. The record tracks source authority, first public appearance, timeline, capabilities, limitations, registry status and graph relationships. Automatic refresh is limited to sources explicitly classified as OFFICIAL and enabled for updates; documentary and research references remain non-authoritative unless reviewed.

Controlled tags

DatasetNLPBenchmarkLanguage Understanding
Relationship graph

IMDb Large Movie Review Dataset

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IMDb Large Movie Review Dataset
Associated organizationOutgoing
Stanford AI Lab

Stanford AI Lab is the organization, project community or institutional context associated with IMDb Large Movie Review Dataset.

Domain contextOutgoing
Language model evaluation and NLP data

IMDb Large Movie Review Dataset belongs to the Language model evaluation and NLP data layer of the intelligent-entity registry.

Machine readable layer

Structured entry data for human tools, AI systems and machine clients.

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    "@type": "Thing",
    "identifier": "ENT-00002958",
    "name": "IMDb Large Movie Review Dataset",
    "alternateName": [],
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    "description": "IMDb Large Movie Review Dataset is a dataset associated with Stanford AI Lab, classified in LXKeys.world as Language / Training Dataset.",
    "creator": "Stanford AI Lab",
    "url": "https://lxkeys.world/entry.php?id=ENT-00002958&lang=en",
    "sameAs": "https://ai.stanford.edu/~amaas/data/sentiment/",
    "image": "",
    "lxkeysWorld": {
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        "classification": "Language / Training Dataset",
        "organization": "Stanford AI Lab",
        "originContext": "Language model evaluation and NLP data",
        "firstPublicAppearance": "2011",
        "currentStatus": "Active",
        "documentationStatus": "Documented",
        "documentationIndex": {
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            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 19,
            "level": "Level V Persistent"
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        "lxCalendarium": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-17T01:48:33+00:00",
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            "updated_utc": "2026-06-17T01:48:33+00:00",
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        "capabilities": [
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            "Benchmark",
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        "timeline": [
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                "date": "2011",
                "title": "Initial dataset release",
                "description": "IMDb Large Movie Review Dataset entered the documented public record in 2011. This event is retained at the precision supported by the Entry’s reviewed source history.",
                "source_url": "https://ai.stanford.edu/~amaas/data/sentiment/",
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        "relationships": [
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                "authority_basis": "curated-corpus-refresh-2026-09-08"
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}
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