{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:PMEEU6UM3MHGVMOF27OWD2HI4I","short_pith_number":"pith:PMEEU6UM","schema_version":"1.0","canonical_sha256":"7b084a7a8cdb0e6ab1c5d7dd61e8e8e2123f9866e353e527e58e1ab2d128af1e","source":{"kind":"arxiv","id":"1606.06259","version":2},"attestation_state":"computed","paper":{"title":"MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CL","authors_text":"Amir Zadeh, Eli Pincus, Louis-Philippe Morency, Rowan Zellers","submitted_at":"2016-06-20T19:23:53Z","abstract_excerpt":"People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific "},"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":"1606.06259","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-20T19:23:53Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"b344b11bc28d72303112b64626d68ba7c9676dbc6319f3c72cfb6e0838331b00","abstract_canon_sha256":"a3c296d52dfa18432378ac73420ba73baeec9a257cdbdc2582dbf9142bfd7b07"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:58:04.033299Z","signature_b64":"A4+1w5P7TdEiccf7fs5Eial4GpsiWIeryEruMx3XAcHsUnNPctyjy5OnmV1VmuLlqRJ1QJjRfRQDiYxWPVm6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b084a7a8cdb0e6ab1c5d7dd61e8e8e2123f9866e353e527e58e1ab2d128af1e","last_reissued_at":"2026-05-18T00:58:04.032723Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:58:04.032723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CL","authors_text":"Amir Zadeh, Eli Pincus, Louis-Philippe Morency, Rowan Zellers","submitted_at":"2016-06-20T19:23:53Z","abstract_excerpt":"People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.06259","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":""},"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":"1606.06259","created_at":"2026-05-18T00:58:04.032811+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.06259v2","created_at":"2026-05-18T00:58:04.032811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.06259","created_at":"2026-05-18T00:58:04.032811+00:00"},{"alias_kind":"pith_short_12","alias_value":"PMEEU6UM3MHG","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"PMEEU6UM3MHGVMOF","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"PMEEU6UM","created_at":"2026-05-18T12:30:39.010887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":9,"sample":[{"citing_arxiv_id":"1907.08696","citing_title":"Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16889","citing_title":"Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities","ref_index":33,"is_internal_anchor":true},{"citing_arxiv_id":"2505.19525","citing_title":"Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2508.20765","citing_title":"Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding","ref_index":111,"is_internal_anchor":true},{"citing_arxiv_id":"2511.06452","citing_title":"MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains","ref_index":65,"is_internal_anchor":true},{"citing_arxiv_id":"2409.07825","citing_title":"Deep Multimodal Learning with Missing Modality: A Survey","ref_index":75,"is_internal_anchor":true},{"citing_arxiv_id":"2511.21331","citing_title":"The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment","ref_index":45,"is_internal_anchor":true},{"citing_arxiv_id":"2512.22991","citing_title":"Fusion or Confusion? Multimodal Complexity Is Not All You Need","ref_index":55,"is_internal_anchor":true},{"citing_arxiv_id":"2603.02123","citing_title":"Nano-EmoX: Unifying Multimodal Emotional Intelligence from Perception to Empathy","ref_index":66,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09468","citing_title":"Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06245","citing_title":"Modality-Aware Contrastive and Uncertainty-Regularized Emotion Recognition","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23348","citing_title":"EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06643","citing_title":"Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12518","citing_title":"Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis","ref_index":62,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06894","citing_title":"McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware","ref_index":87,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I","json":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I.json","graph_json":"https://pith.science/api/pith-number/PMEEU6UM3MHGVMOF27OWD2HI4I/graph.json","events_json":"https://pith.science/api/pith-number/PMEEU6UM3MHGVMOF27OWD2HI4I/events.json","paper":"https://pith.science/paper/PMEEU6UM"},"agent_actions":{"view_html":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I","download_json":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I.json","view_paper":"https://pith.science/paper/PMEEU6UM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.06259&json=true","fetch_graph":"https://pith.science/api/pith-number/PMEEU6UM3MHGVMOF27OWD2HI4I/graph.json","fetch_events":"https://pith.science/api/pith-number/PMEEU6UM3MHGVMOF27OWD2HI4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I/action/storage_attestation","attest_author":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I/action/author_attestation","sign_citation":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I/action/citation_signature","submit_replication":"https://pith.science/pith/PMEEU6UM3MHGVMOF27OWD2HI4I/action/replication_record"}},"created_at":"2026-05-18T00:58:04.032811+00:00","updated_at":"2026-05-18T00:58:04.032811+00:00"}