{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GP5OZA23NBW6NXT6DHQ5SK2BS4","short_pith_number":"pith:GP5OZA23","schema_version":"1.0","canonical_sha256":"33faec835b686de6de7e19e1d92b41973b63dbe242f815ae8e33e3db4eaff5c0","source":{"kind":"arxiv","id":"2208.09417","version":2},"attestation_state":"computed","paper":{"title":"Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jeff Z. Pan, Jiaoyan Chen, Wen Zhang, Yufeng Huang, Zhen Yao, Zhuo Chen","submitted_at":"2022-08-19T16:04:29Z","abstract_excerpt":"Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, in this paper we propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene gra"},"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":"2208.09417","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-19T16:04:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5a8073358120d4fb33ff1e53a04c9c054491ad06bbb33af3a6f3a58be9850820","abstract_canon_sha256":"5e711e9431832c53c4498d349e8c881816fc6385380f992aab5f3426464f8d41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:33:40.888735Z","signature_b64":"kO28ubvwg5gRKm1v9Drv+vXFZiLSFs9YmcCI86yD/I1PtJlmkCRiFkDrfPiwcyhH/r7dErcfrPY028Wt9HD/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33faec835b686de6de7e19e1d92b41973b63dbe242f815ae8e33e3db4eaff5c0","last_reissued_at":"2026-07-05T06:33:40.888240Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:33:40.888240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jeff Z. Pan, Jiaoyan Chen, Wen Zhang, Yufeng Huang, Zhen Yao, Zhuo Chen","submitted_at":"2022-08-19T16:04:29Z","abstract_excerpt":"Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, in this paper we propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene gra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09417","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.09417/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2208.09417","created_at":"2026-07-05T06:33:40.888303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09417v2","created_at":"2026-07-05T06:33:40.888303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09417","created_at":"2026-07-05T06:33:40.888303+00:00"},{"alias_kind":"pith_short_12","alias_value":"GP5OZA23NBW6","created_at":"2026-07-05T06:33:40.888303+00:00"},{"alias_kind":"pith_short_16","alias_value":"GP5OZA23NBW6NXT6","created_at":"2026-07-05T06:33:40.888303+00:00"},{"alias_kind":"pith_short_8","alias_value":"GP5OZA23","created_at":"2026-07-05T06:33:40.888303+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/GP5OZA23NBW6NXT6DHQ5SK2BS4","json":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4.json","graph_json":"https://pith.science/api/pith-number/GP5OZA23NBW6NXT6DHQ5SK2BS4/graph.json","events_json":"https://pith.science/api/pith-number/GP5OZA23NBW6NXT6DHQ5SK2BS4/events.json","paper":"https://pith.science/paper/GP5OZA23"},"agent_actions":{"view_html":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4","download_json":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4.json","view_paper":"https://pith.science/paper/GP5OZA23","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09417&json=true","fetch_graph":"https://pith.science/api/pith-number/GP5OZA23NBW6NXT6DHQ5SK2BS4/graph.json","fetch_events":"https://pith.science/api/pith-number/GP5OZA23NBW6NXT6DHQ5SK2BS4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4/action/storage_attestation","attest_author":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4/action/author_attestation","sign_citation":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4/action/citation_signature","submit_replication":"https://pith.science/pith/GP5OZA23NBW6NXT6DHQ5SK2BS4/action/replication_record"}},"created_at":"2026-07-05T06:33:40.888303+00:00","updated_at":"2026-07-05T06:33:40.888303+00:00"}