{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:VWRHY23ZB6P7I3L3DSRNZJETTF","short_pith_number":"pith:VWRHY23Z","schema_version":"1.0","canonical_sha256":"ada27c6b790f9ff46d7b1ca2dca49399562ae850222d3f0bf27eaa6bc9d55e4a","source":{"kind":"arxiv","id":"1502.03322","version":1},"attestation_state":"computed","paper":{"title":"Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Min Zhang, Shaoping Ma, Yiqun Liu, Yongfeng Zhang","submitted_at":"2015-02-11T14:45:41Z","abstract_excerpt":"Sentiment analysis on user reviews helps to keep track of user reactions towards products, and make advices to users about what to buy. State-of-the-art review-level sentiment classification techniques could give pretty good precisions of above 90%. However, current phrase-level sentiment analysis approaches might only give sentiment polarity labelling precisions of around 70%~80%, which is far from satisfaction and restricts its application in many practical tasks. In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1502.03322","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-02-11T14:45:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"36915b71cf0ad10467ced8dc7eba3c97c13a7ee39d76db2bc873da3c2a805fad","abstract_canon_sha256":"ae6712718bf048b495fac7d1e025965c6d0b390095f256422980c9c79a077b22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:27:19.713505Z","signature_b64":"sUEUJ8a4Lg5SvHlOZUEK4UY2o24CDZHNi+NSaDTYom1jIskFhpIWw4hyhKg2jpKVqVaJCLt//8g+max6a1ctBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ada27c6b790f9ff46d7b1ca2dca49399562ae850222d3f0bf27eaa6bc9d55e4a","last_reissued_at":"2026-05-18T02:27:19.712766Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:27:19.712766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Min Zhang, Shaoping Ma, Yiqun Liu, Yongfeng Zhang","submitted_at":"2015-02-11T14:45:41Z","abstract_excerpt":"Sentiment analysis on user reviews helps to keep track of user reactions towards products, and make advices to users about what to buy. State-of-the-art review-level sentiment classification techniques could give pretty good precisions of above 90%. However, current phrase-level sentiment analysis approaches might only give sentiment polarity labelling precisions of around 70%~80%, which is far from satisfaction and restricts its application in many practical tasks. In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.03322","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1502.03322","created_at":"2026-05-18T02:27:19.712869+00:00"},{"alias_kind":"arxiv_version","alias_value":"1502.03322v1","created_at":"2026-05-18T02:27:19.712869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.03322","created_at":"2026-05-18T02:27:19.712869+00:00"},{"alias_kind":"pith_short_12","alias_value":"VWRHY23ZB6P7","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_16","alias_value":"VWRHY23ZB6P7I3L3","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_8","alias_value":"VWRHY23Z","created_at":"2026-05-18T12:29:47.479230+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF","json":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF.json","graph_json":"https://pith.science/api/pith-number/VWRHY23ZB6P7I3L3DSRNZJETTF/graph.json","events_json":"https://pith.science/api/pith-number/VWRHY23ZB6P7I3L3DSRNZJETTF/events.json","paper":"https://pith.science/paper/VWRHY23Z"},"agent_actions":{"view_html":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF","download_json":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF.json","view_paper":"https://pith.science/paper/VWRHY23Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1502.03322&json=true","fetch_graph":"https://pith.science/api/pith-number/VWRHY23ZB6P7I3L3DSRNZJETTF/graph.json","fetch_events":"https://pith.science/api/pith-number/VWRHY23ZB6P7I3L3DSRNZJETTF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF/action/storage_attestation","attest_author":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF/action/author_attestation","sign_citation":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF/action/citation_signature","submit_replication":"https://pith.science/pith/VWRHY23ZB6P7I3L3DSRNZJETTF/action/replication_record"}},"created_at":"2026-05-18T02:27:19.712869+00:00","updated_at":"2026-05-18T02:27:19.712869+00:00"}