{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:I7RA75GKFXITYQD5V2PZ5RZIXT","short_pith_number":"pith:I7RA75GK","schema_version":"1.0","canonical_sha256":"47e20ff4ca2dd13c407dae9f9ec728bceba025111f0796bfe9b32213cbcacd20","source":{"kind":"arxiv","id":"2410.14150","version":1},"attestation_state":"computed","paper":{"title":"Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Heli Sun, Qunshu Gao, Ruichen Cao, Wenjie Huang, Xiaoyong Huang","submitted_at":"2024-10-18T03:40:45Z","abstract_excerpt":"With the rapid development of the internet, the richness of User-Generated Contentcontinues to increase, making Multimodal Aspect-Based Sentiment Analysis (MABSA) a research hotspot. Existing studies have achieved certain results in MABSA, but they have not effectively addressed the analytical challenges in scenarios where multiple entities and sentiments coexist. This paper innovatively introduces Large Language Models (LLMs) for event decomposition and proposes a reinforcement learning framework for Multimodal Aspect-based Sentiment Analysis (MABSA-RL) framework. This framework decomposes th"},"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":"2410.14150","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-18T03:40:45Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"9d1bdf102230984e702f50d23f97f72cd08f6565273ca4a3eb71c8d2845ae036","abstract_canon_sha256":"6c30f5cf585f1690faa7e67afff82dfacbbc17e18107f81dcdbbbe56e75a45d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:27.975968Z","signature_b64":"1t+AJxjN5yHMqE41wSs8YdmhMSB2uK+sverSgrlgpE5lL99RWrXzulYiqIf+jD7v1W+zexv20KgQZFEQeAcQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47e20ff4ca2dd13c407dae9f9ec728bceba025111f0796bfe9b32213cbcacd20","last_reissued_at":"2026-07-05T09:22:27.975539Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:27.975539Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Heli Sun, Qunshu Gao, Ruichen Cao, Wenjie Huang, Xiaoyong Huang","submitted_at":"2024-10-18T03:40:45Z","abstract_excerpt":"With the rapid development of the internet, the richness of User-Generated Contentcontinues to increase, making Multimodal Aspect-Based Sentiment Analysis (MABSA) a research hotspot. Existing studies have achieved certain results in MABSA, but they have not effectively addressed the analytical challenges in scenarios where multiple entities and sentiments coexist. This paper innovatively introduces Large Language Models (LLMs) for event decomposition and proposes a reinforcement learning framework for Multimodal Aspect-based Sentiment Analysis (MABSA-RL) framework. This framework decomposes th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14150","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.14150/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":"2410.14150","created_at":"2026-07-05T09:22:27.975596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14150v1","created_at":"2026-07-05T09:22:27.975596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14150","created_at":"2026-07-05T09:22:27.975596+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7RA75GKFXIT","created_at":"2026-07-05T09:22:27.975596+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7RA75GKFXITYQD5","created_at":"2026-07-05T09:22:27.975596+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7RA75GK","created_at":"2026-07-05T09:22:27.975596+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.07086","citing_title":"Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT","json":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT.json","graph_json":"https://pith.science/api/pith-number/I7RA75GKFXITYQD5V2PZ5RZIXT/graph.json","events_json":"https://pith.science/api/pith-number/I7RA75GKFXITYQD5V2PZ5RZIXT/events.json","paper":"https://pith.science/paper/I7RA75GK"},"agent_actions":{"view_html":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT","download_json":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT.json","view_paper":"https://pith.science/paper/I7RA75GK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14150&json=true","fetch_graph":"https://pith.science/api/pith-number/I7RA75GKFXITYQD5V2PZ5RZIXT/graph.json","fetch_events":"https://pith.science/api/pith-number/I7RA75GKFXITYQD5V2PZ5RZIXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT/action/storage_attestation","attest_author":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT/action/author_attestation","sign_citation":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT/action/citation_signature","submit_replication":"https://pith.science/pith/I7RA75GKFXITYQD5V2PZ5RZIXT/action/replication_record"}},"created_at":"2026-07-05T09:22:27.975596+00:00","updated_at":"2026-07-05T09:22:27.975596+00:00"}