{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NRFY4USEXMIHJ3R5JZELN6VIPN","short_pith_number":"pith:NRFY4USE","schema_version":"1.0","canonical_sha256":"6c4b8e5244bb1074ee3d4e48b6faa87b780615a5ed266781222bbcac8cd19839","source":{"kind":"arxiv","id":"1704.06125","version":1},"attestation_state":"computed","paper":{"title":"BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CL","authors_text":"Mathieu Cliche","submitted_at":"2017-04-20T13:10:25Z","abstract_excerpt":"In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the "},"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":"1704.06125","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-20T13:10:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f20024b12e0f5805862d4fc3edc42e3b911ae60926bc6c5031bc7c504f42444c","abstract_canon_sha256":"6dc8d350b8c9a91e5ede804f802d44da5e37cc193231c2b37bb7a2fcf0976c20"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:03.235415Z","signature_b64":"NWZ+5+blBWaBm7QliJrHjZXbWkHkd9EHrgiFyZP/xHJxgCaA2P+LiqTz5IGofO6w8iw41gIHsLIt25ZGy3wRCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c4b8e5244bb1074ee3d4e48b6faa87b780615a5ed266781222bbcac8cd19839","last_reissued_at":"2026-05-18T00:46:03.234935Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:03.234935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CL","authors_text":"Mathieu Cliche","submitted_at":"2017-04-20T13:10:25Z","abstract_excerpt":"In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06125","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":"1704.06125","created_at":"2026-05-18T00:46:03.235008+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.06125v1","created_at":"2026-05-18T00:46:03.235008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06125","created_at":"2026-05-18T00:46:03.235008+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRFY4USEXMIH","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRFY4USEXMIHJ3R5","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRFY4USE","created_at":"2026-05-18T12:31:34.259226+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/NRFY4USEXMIHJ3R5JZELN6VIPN","json":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN.json","graph_json":"https://pith.science/api/pith-number/NRFY4USEXMIHJ3R5JZELN6VIPN/graph.json","events_json":"https://pith.science/api/pith-number/NRFY4USEXMIHJ3R5JZELN6VIPN/events.json","paper":"https://pith.science/paper/NRFY4USE"},"agent_actions":{"view_html":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN","download_json":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN.json","view_paper":"https://pith.science/paper/NRFY4USE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.06125&json=true","fetch_graph":"https://pith.science/api/pith-number/NRFY4USEXMIHJ3R5JZELN6VIPN/graph.json","fetch_events":"https://pith.science/api/pith-number/NRFY4USEXMIHJ3R5JZELN6VIPN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN/action/storage_attestation","attest_author":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN/action/author_attestation","sign_citation":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN/action/citation_signature","submit_replication":"https://pith.science/pith/NRFY4USEXMIHJ3R5JZELN6VIPN/action/replication_record"}},"created_at":"2026-05-18T00:46:03.235008+00:00","updated_at":"2026-05-18T00:46:03.235008+00:00"}