{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PA25OCBUGCEACDJSNA7MG4OO23","short_pith_number":"pith:PA25OCBU","schema_version":"1.0","canonical_sha256":"7835d708343088010d32683ec371ced6c27b64e0fe9505faa856ddca78374f0f","source":{"kind":"arxiv","id":"2301.09279","version":2},"attestation_state":"computed","paper":{"title":"StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-fin.CP"],"primary_cat":"cs.CL","authors_text":"Hoyoul Luis Youn, Jean Lee, Josiah Poon, Soyeon Caren Han","submitted_at":"2023-01-23T05:32:42Z","abstract_excerpt":"There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited. This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market that consists of 10,000 English comments collected from StockTwits, a financial social media platform. Inspired by behavioral finance, it proposes 12 fine-grained emotion classes that span the roller coaster of investor emotion. Unlike existing financial sentiment datasets, StockEmotions presents granular features such as investor sentiment classes, fine-grained emotions, emoji"},"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":"2301.09279","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-01-23T05:32:42Z","cross_cats_sorted":["cs.AI","cs.LG","q-fin.CP"],"title_canon_sha256":"af405c26b88a674444a188f59edf90f941158ac3aad566d27eea3e1c2918260d","abstract_canon_sha256":"6c44b4d3a8cf26d500ecb0667e7a69fce335a477c59fa640c7bf87e74cc078aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:23.885962Z","signature_b64":"FXHwfBgf/2X3bj0c8eXbYphbV32BAlqck4eiehNzBJaTbt1NMg9CaP4e6BlRwgVpD1GZTjrkGTrtsU7xTGooAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7835d708343088010d32683ec371ced6c27b64e0fe9505faa856ddca78374f0f","last_reissued_at":"2026-07-05T07:17:23.885517Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:23.885517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-fin.CP"],"primary_cat":"cs.CL","authors_text":"Hoyoul Luis Youn, Jean Lee, Josiah Poon, Soyeon Caren Han","submitted_at":"2023-01-23T05:32:42Z","abstract_excerpt":"There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited. This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market that consists of 10,000 English comments collected from StockTwits, a financial social media platform. Inspired by behavioral finance, it proposes 12 fine-grained emotion classes that span the roller coaster of investor emotion. Unlike existing financial sentiment datasets, StockEmotions presents granular features such as investor sentiment classes, fine-grained emotions, emoji"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09279","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/2301.09279/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":"2301.09279","created_at":"2026-07-05T07:17:23.885584+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.09279v2","created_at":"2026-07-05T07:17:23.885584+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09279","created_at":"2026-07-05T07:17:23.885584+00:00"},{"alias_kind":"pith_short_12","alias_value":"PA25OCBUGCEA","created_at":"2026-07-05T07:17:23.885584+00:00"},{"alias_kind":"pith_short_16","alias_value":"PA25OCBUGCEACDJS","created_at":"2026-07-05T07:17:23.885584+00:00"},{"alias_kind":"pith_short_8","alias_value":"PA25OCBU","created_at":"2026-07-05T07:17:23.885584+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.03092","citing_title":"Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23","json":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23.json","graph_json":"https://pith.science/api/pith-number/PA25OCBUGCEACDJSNA7MG4OO23/graph.json","events_json":"https://pith.science/api/pith-number/PA25OCBUGCEACDJSNA7MG4OO23/events.json","paper":"https://pith.science/paper/PA25OCBU"},"agent_actions":{"view_html":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23","download_json":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23.json","view_paper":"https://pith.science/paper/PA25OCBU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.09279&json=true","fetch_graph":"https://pith.science/api/pith-number/PA25OCBUGCEACDJSNA7MG4OO23/graph.json","fetch_events":"https://pith.science/api/pith-number/PA25OCBUGCEACDJSNA7MG4OO23/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23/action/storage_attestation","attest_author":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23/action/author_attestation","sign_citation":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23/action/citation_signature","submit_replication":"https://pith.science/pith/PA25OCBUGCEACDJSNA7MG4OO23/action/replication_record"}},"created_at":"2026-07-05T07:17:23.885584+00:00","updated_at":"2026-07-05T07:17:23.885584+00:00"}