{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:LPO5RYQP4BOZ5LXFBFG4QWQB2Y","short_pith_number":"pith:LPO5RYQP","schema_version":"1.0","canonical_sha256":"5bddd8e20fe05d9eaee5094dc85a01d630712c67b5ee448355f7199ec93558c2","source":{"kind":"arxiv","id":"2010.01309","version":1},"attestation_state":"computed","paper":{"title":"Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Amirmohammad Kazameini, Erik Cambria, Samin Fatehi, Sauleh Eetemadi, Yash Mehta","submitted_at":"2020-10-03T09:25:51Z","abstract_excerpt":"Recently, the automatic prediction of personality traits has received increasing attention and has emerged as a hot topic within the field of affective computing. In this work, we present a novel deep learning-based approach for automated personality detection from text. We leverage state of the art advances in natural language understanding, namely the BERT language model to extract contextualized word embeddings from textual data for automated author personality detection. Our primary goal is to develop a computationally efficient, high-performance personality prediction model which can be e"},"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":"2010.01309","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-03T09:25:51Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"6713b24d681c91fdd8cd3049a3a8dc9ef86d892752b6df3a43e2c253264fe9f5","abstract_canon_sha256":"c290a3f61f5b746ffae445c4b8eb0d88ebf39105d953086e122af8ee5e515093"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:04.087249Z","signature_b64":"tYCnMQpORsQglbQATBD2mZo1AnP7jD4fe+HeNGMY8/uhuR+BNvrjh2BVW425pk3kvHln8q9aGmLwzHa/Fdp7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bddd8e20fe05d9eaee5094dc85a01d630712c67b5ee448355f7199ec93558c2","last_reissued_at":"2026-07-05T01:40:04.086820Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:04.086820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Amirmohammad Kazameini, Erik Cambria, Samin Fatehi, Sauleh Eetemadi, Yash Mehta","submitted_at":"2020-10-03T09:25:51Z","abstract_excerpt":"Recently, the automatic prediction of personality traits has received increasing attention and has emerged as a hot topic within the field of affective computing. In this work, we present a novel deep learning-based approach for automated personality detection from text. We leverage state of the art advances in natural language understanding, namely the BERT language model to extract contextualized word embeddings from textual data for automated author personality detection. Our primary goal is to develop a computationally efficient, high-performance personality prediction model which can be e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.01309","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.01309/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":"2010.01309","created_at":"2026-07-05T01:40:04.086874+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.01309v1","created_at":"2026-07-05T01:40:04.086874+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.01309","created_at":"2026-07-05T01:40:04.086874+00:00"},{"alias_kind":"pith_short_12","alias_value":"LPO5RYQP4BOZ","created_at":"2026-07-05T01:40:04.086874+00:00"},{"alias_kind":"pith_short_16","alias_value":"LPO5RYQP4BOZ5LXF","created_at":"2026-07-05T01:40:04.086874+00:00"},{"alias_kind":"pith_short_8","alias_value":"LPO5RYQP","created_at":"2026-07-05T01:40:04.086874+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.21087","citing_title":"Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?","ref_index":60,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y","json":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y.json","graph_json":"https://pith.science/api/pith-number/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/graph.json","events_json":"https://pith.science/api/pith-number/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/events.json","paper":"https://pith.science/paper/LPO5RYQP"},"agent_actions":{"view_html":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y","download_json":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y.json","view_paper":"https://pith.science/paper/LPO5RYQP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.01309&json=true","fetch_graph":"https://pith.science/api/pith-number/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/action/storage_attestation","attest_author":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/action/author_attestation","sign_citation":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/action/citation_signature","submit_replication":"https://pith.science/pith/LPO5RYQP4BOZ5LXFBFG4QWQB2Y/action/replication_record"}},"created_at":"2026-07-05T01:40:04.086874+00:00","updated_at":"2026-07-05T01:40:04.086874+00:00"}