{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:37USK235MUZQ5QF35LU7MP5E53","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a96889e63a8a3cd3e2bf07214e1d8fb0bbee11cdf8b9f40fc703cfd3f5154b2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-14T21:04:15Z","title_canon_sha256":"96321d20fb5b5e8703ac16b7e34803ee9ed0347abbda9f57219adc5373f28955"},"schema_version":"1.0","source":{"id":"1802.05322","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05322","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05322v1","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05322","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"pith_short_12","alias_value":"37USK235MUZQ","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"37USK235MUZQ5QF3","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"37USK235","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:059aae493da4e1afb800bba53ec4ea5dc1fbb890e64d2f30859bb7105d6baf9c","target":"graph","created_at":"2026-05-18T00:23:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"This paper proposes a method for classifying movie genres by only looking at text reviews. The data used are from Large Movie Review Dataset v1.0 and IMDb. This paper compared a K-nearest neighbors (KNN) model and a multilayer perceptron (MLP) that uses tf-idf as input features. The paper also discusses different evaluation metrics used when doing multi-label classification. For the data used in this research, the KNN model performed the best with an accuracy of 55.4\\% and a Hamming loss of 0.047.","authors_text":"Adam Nyberg","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-14T21:04:15Z","title":"Classifying movie genres by analyzing text reviews"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05322","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:94730b4e0be2f205a7b4a88590bea80ed610eb59c30f75caccd016dbe6506898","target":"record","created_at":"2026-05-18T00:23:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a96889e63a8a3cd3e2bf07214e1d8fb0bbee11cdf8b9f40fc703cfd3f5154b2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-14T21:04:15Z","title_canon_sha256":"96321d20fb5b5e8703ac16b7e34803ee9ed0347abbda9f57219adc5373f28955"},"schema_version":"1.0","source":{"id":"1802.05322","kind":"arxiv","version":1}},"canonical_sha256":"dfe9256b7d65330ec0bbeae9f63fa4eedde1484d0c7311fbfd132526087962de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dfe9256b7d65330ec0bbeae9f63fa4eedde1484d0c7311fbfd132526087962de","first_computed_at":"2026-05-18T00:23:15.985027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:15.985027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1JM+fvrr/8ES3ZTnoApc6pHsaiVoVrfTzEejfiaM82xSvVhlCZjewzjSv1uufua36vNVS29rt7entHQSPoJVDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:15.985578Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.05322","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94730b4e0be2f205a7b4a88590bea80ed610eb59c30f75caccd016dbe6506898","sha256:059aae493da4e1afb800bba53ec4ea5dc1fbb890e64d2f30859bb7105d6baf9c"],"state_sha256":"3f92ade144eb34e9aab64abc0b079d61e57b9395e965c065ccdac6b0bb5d4615"}