{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FT4G5DWEJDAGLTJQ3QECM4JZ6A","short_pith_number":"pith:FT4G5DWE","canonical_record":{"source":{"id":"1708.03995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-14T02:40:20Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"d494a4d1518256ecec1267267f93c5a96729b08712c6cb4a05730e905c128141","abstract_canon_sha256":"68c444857af35f4b5c0be770914d20a1b2e46964e136c0bbda67eaade00f94fe"},"schema_version":"1.0"},"canonical_sha256":"2cf86e8ec448c065cd30dc08267139f0105d63b3c9c66b40ac2b2429e66e1a97","source":{"kind":"arxiv","id":"1708.03995","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03995","created_at":"2026-05-18T00:16:08Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03995v1","created_at":"2026-05-18T00:16:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03995","created_at":"2026-05-18T00:16:08Z"},{"alias_kind":"pith_short_12","alias_value":"FT4G5DWEJDAG","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FT4G5DWEJDAGLTJQ","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FT4G5DWE","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FT4G5DWEJDAGLTJQ3QECM4JZ6A","target":"record","payload":{"canonical_record":{"source":{"id":"1708.03995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-14T02:40:20Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"d494a4d1518256ecec1267267f93c5a96729b08712c6cb4a05730e905c128141","abstract_canon_sha256":"68c444857af35f4b5c0be770914d20a1b2e46964e136c0bbda67eaade00f94fe"},"schema_version":"1.0"},"canonical_sha256":"2cf86e8ec448c065cd30dc08267139f0105d63b3c9c66b40ac2b2429e66e1a97","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:08.818492Z","signature_b64":"Qqc2Xx1jxWZqpToeCw/wSsRcmmY/w8uzDH/MSntFIeeYdulSJipoqT0Yu3WNQJwcu0H0vKqoaejsxqstjDjPCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cf86e8ec448c065cd30dc08267139f0105d63b3c9c66b40ac2b2429e66e1a97","last_reissued_at":"2026-05-18T00:16:08.817726Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:08.817726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.03995","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uJlIfGrLHl/Dgb7fT7rqknzz2/N1jPM6uABufSUGyt8mqlFWCK7dP2NVLUrM7kffSTYh60mS7GhL9cj7VGusCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T11:31:02.094473Z"},"content_sha256":"95670f9f9f8db4aed7d44d2fc17513713f29492d04f1dab6f4262b5940309286","schema_version":"1.0","event_id":"sha256:95670f9f9f8db4aed7d44d2fc17513713f29492d04f1dab6f4262b5940309286"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FT4G5DWEJDAGLTJQ3QECM4JZ6A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sentiment Analysis by Joint Learning of Word Embeddings and Classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Bill Sethares, Prathusha Kameswara Sarma","submitted_at":"2017-08-14T02:40:20Z","abstract_excerpt":"Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current state of the art al- gorithms for learning word embeddings learn vector representations from large corpora of text documents in an unsu- pervised fashion. This paper introduces SWESA (Supervised Word Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via word embeddings. SWESA leverages document label infor- mation to learn vector representations of words from a modest corpus of text doc- uments"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03995","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O9VIjWkeut5PSmiKKQZqeWH/OBeOkxWHyYODPd78EOMeAeEtIWL32haafHeEqvDFHZWDhlwJ7LRQUBc1kqgTDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T11:31:02.095039Z"},"content_sha256":"8fa620920d6ca391a45ad3a2989c97438462a174377a5fb22bea5ac929edaabf","schema_version":"1.0","event_id":"sha256:8fa620920d6ca391a45ad3a2989c97438462a174377a5fb22bea5ac929edaabf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A/bundle.json","state_url":"https://pith.science/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T11:31:02Z","links":{"resolver":"https://pith.science/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A","bundle":"https://pith.science/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A/bundle.json","state":"https://pith.science/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FT4G5DWEJDAGLTJQ3QECM4JZ6A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FT4G5DWEJDAGLTJQ3QECM4JZ6A","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"68c444857af35f4b5c0be770914d20a1b2e46964e136c0bbda67eaade00f94fe","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-14T02:40:20Z","title_canon_sha256":"d494a4d1518256ecec1267267f93c5a96729b08712c6cb4a05730e905c128141"},"schema_version":"1.0","source":{"id":"1708.03995","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.03995","created_at":"2026-05-18T00:16:08Z"},{"alias_kind":"arxiv_version","alias_value":"1708.03995v1","created_at":"2026-05-18T00:16:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03995","created_at":"2026-05-18T00:16:08Z"},{"alias_kind":"pith_short_12","alias_value":"FT4G5DWEJDAG","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FT4G5DWEJDAGLTJQ","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FT4G5DWE","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:8fa620920d6ca391a45ad3a2989c97438462a174377a5fb22bea5ac929edaabf","target":"graph","created_at":"2026-05-18T00:16:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current state of the art al- gorithms for learning word embeddings learn vector representations from large corpora of text documents in an unsu- pervised fashion. This paper introduces SWESA (Supervised Word Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via word embeddings. SWESA leverages document label infor- mation to learn vector representations of words from a modest corpus of text doc- uments","authors_text":"Bill Sethares, Prathusha Kameswara Sarma","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-14T02:40:20Z","title":"Sentiment Analysis by Joint Learning of Word Embeddings and Classifier"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03995","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:95670f9f9f8db4aed7d44d2fc17513713f29492d04f1dab6f4262b5940309286","target":"record","created_at":"2026-05-18T00:16:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"68c444857af35f4b5c0be770914d20a1b2e46964e136c0bbda67eaade00f94fe","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-14T02:40:20Z","title_canon_sha256":"d494a4d1518256ecec1267267f93c5a96729b08712c6cb4a05730e905c128141"},"schema_version":"1.0","source":{"id":"1708.03995","kind":"arxiv","version":1}},"canonical_sha256":"2cf86e8ec448c065cd30dc08267139f0105d63b3c9c66b40ac2b2429e66e1a97","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2cf86e8ec448c065cd30dc08267139f0105d63b3c9c66b40ac2b2429e66e1a97","first_computed_at":"2026-05-18T00:16:08.817726Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:08.817726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qqc2Xx1jxWZqpToeCw/wSsRcmmY/w8uzDH/MSntFIeeYdulSJipoqT0Yu3WNQJwcu0H0vKqoaejsxqstjDjPCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:08.818492Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.03995","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95670f9f9f8db4aed7d44d2fc17513713f29492d04f1dab6f4262b5940309286","sha256:8fa620920d6ca391a45ad3a2989c97438462a174377a5fb22bea5ac929edaabf"],"state_sha256":"f5400b9b373a2b96708de35874ef1e5a70c703ecd8a165880f54b270e0590be6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iFQSfsdoT8J+O1rt+Ip0v0ndKbj5UBwQWlqcYbkqxhHlxdt0dzQfk7DK3399ITyjgVn1X7TlXT9qw+YcLQOsCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T11:31:02.100050Z","bundle_sha256":"c7aee623fe4be8a067d26e1de2c40fb5c8803fec7dd2178e79e3475399024b2a"}}