{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NO2BSRK4QVQ7QZ7KDN5MC7LG7V","short_pith_number":"pith:NO2BSRK4","schema_version":"1.0","canonical_sha256":"6bb419455c8561f867ea1b7ac17d66fd6c4aa8d9deb63ab71f5ee111c9d6c8cc","source":{"kind":"arxiv","id":"1906.04501","version":1},"attestation_state":"computed","paper":{"title":"Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Linlin Houb, Ou Wua, Pinlong Zhaoa","submitted_at":"2019-06-11T11:26:25Z","abstract_excerpt":"Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, we find such dependency information between different aspects can bring additional valuable information. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one s"},"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":"1906.04501","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-11T11:26:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6a04512710ecefbe57c6cbe367ebac94aeae15e8212ccfca9888621d8c2c0908","abstract_canon_sha256":"15bb3ba3286f15122f4a29f876bcdbf12b0bb8768cfc4f8900f1db48dd35c70c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:39.128501Z","signature_b64":"f2+GoPpLt4C3HububIEXMgulZIQg90KZ4iH0gG+y1bGDseN3AlNk4BCUEdiBaF+DtF6qbRtFhtBFeylW73snAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bb419455c8561f867ea1b7ac17d66fd6c4aa8d9deb63ab71f5ee111c9d6c8cc","last_reissued_at":"2026-05-17T23:43:39.128022Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:39.128022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Linlin Houb, Ou Wua, Pinlong Zhaoa","submitted_at":"2019-06-11T11:26:25Z","abstract_excerpt":"Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, we find such dependency information between different aspects can bring additional valuable information. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04501","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":"1906.04501","created_at":"2026-05-17T23:43:39.128091+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04501v1","created_at":"2026-05-17T23:43:39.128091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04501","created_at":"2026-05-17T23:43:39.128091+00:00"},{"alias_kind":"pith_short_12","alias_value":"NO2BSRK4QVQ7","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NO2BSRK4QVQ7QZ7K","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NO2BSRK4","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.11860","citing_title":"Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V","json":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V.json","graph_json":"https://pith.science/api/pith-number/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/graph.json","events_json":"https://pith.science/api/pith-number/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/events.json","paper":"https://pith.science/paper/NO2BSRK4"},"agent_actions":{"view_html":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V","download_json":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V.json","view_paper":"https://pith.science/paper/NO2BSRK4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04501&json=true","fetch_graph":"https://pith.science/api/pith-number/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/graph.json","fetch_events":"https://pith.science/api/pith-number/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/action/storage_attestation","attest_author":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/action/author_attestation","sign_citation":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/action/citation_signature","submit_replication":"https://pith.science/pith/NO2BSRK4QVQ7QZ7KDN5MC7LG7V/action/replication_record"}},"created_at":"2026-05-17T23:43:39.128091+00:00","updated_at":"2026-05-17T23:43:39.128091+00:00"}