{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EK3KXSAX577GZGWYCSESEUDVL2","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":"3923ec402a9e16f90480606cc681bfdb49465fc903bfd161b1eb6de647c4e6a2","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-15T11:14:47Z","title_canon_sha256":"f116a6b4281251afbd8dc598475ac41008398f2dc4a76ef5a83cf0dbb7fd0497"},"schema_version":"1.0","source":{"id":"2205.07259","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.07259","created_at":"2026-07-05T04:23:26Z"},{"alias_kind":"arxiv_version","alias_value":"2205.07259v1","created_at":"2026-07-05T04:23:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.07259","created_at":"2026-07-05T04:23:26Z"},{"alias_kind":"pith_short_12","alias_value":"EK3KXSAX577G","created_at":"2026-07-05T04:23:26Z"},{"alias_kind":"pith_short_16","alias_value":"EK3KXSAX577GZGWY","created_at":"2026-07-05T04:23:26Z"},{"alias_kind":"pith_short_8","alias_value":"EK3KXSAX","created_at":"2026-07-05T04:23:26Z"}],"graph_snapshots":[{"event_id":"sha256:89563c24aeadbfb122b66042fceb10bcf32cc0eb288cda7fcf30d5f1eddf837c","target":"graph","created_at":"2026-07-05T04:23:26Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.07259/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated with topics/aspects to provide efficient customer assistance. LDA and LSA fail to capture the semantic relationship and are not specific to any domain. In this study, we evaluate BERTopic, a novel method that generates topics using sentence embeddings on Consumer Financial Protection Bureau (CFPB) data. Our work shows that BERTopic is flexible and yet provides meaningful and diverse topics compare","authors_text":"Bharath Kumar Bolla, Deepak Kumar Nayak, Jyothsna Kh, Vasudeva Raju Sangaraju","cross_cats":["cs.AI","cs.CL","cs.IR","cs.IT","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-15T11:14:47Z","title":"Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.07259","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:cb9ee062367e9f3c47650b89a3d9a0b47b5021ad957f7d852faae18413f2dec8","target":"record","created_at":"2026-07-05T04:23:26Z","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":"3923ec402a9e16f90480606cc681bfdb49465fc903bfd161b1eb6de647c4e6a2","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-15T11:14:47Z","title_canon_sha256":"f116a6b4281251afbd8dc598475ac41008398f2dc4a76ef5a83cf0dbb7fd0497"},"schema_version":"1.0","source":{"id":"2205.07259","kind":"arxiv","version":1}},"canonical_sha256":"22b6abc817effe6c9ad814892250755e8f2dc4905db9504ee4d6d81b2fde6c97","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22b6abc817effe6c9ad814892250755e8f2dc4905db9504ee4d6d81b2fde6c97","first_computed_at":"2026-07-05T04:23:26.998556Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:23:26.998556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yr7CL5+lQB7rLCfOS3l8scjYHMqnlrwy1Ng1Sj3dUKJNH12pFvDx1BDjh9lY9UvhjLf+2L19ZKbUPMA9GkvtAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:23:26.999072Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.07259","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb9ee062367e9f3c47650b89a3d9a0b47b5021ad957f7d852faae18413f2dec8","sha256:89563c24aeadbfb122b66042fceb10bcf32cc0eb288cda7fcf30d5f1eddf837c"],"state_sha256":"1a028e7b129629550c04f18686fcae12a4266e11952062cdef4ae66b58a785fb"}