{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QQNNUC6SY4SZEASR5VOOCLXHUS","short_pith_number":"pith:QQNNUC6S","schema_version":"1.0","canonical_sha256":"841ada0bd2c725920251ed5ce12ee7a4acb67c1f43adf352ca598e20cb185cc9","source":{"kind":"arxiv","id":"1902.02507","version":3},"attestation_state":"computed","paper":{"title":"Towards Autoencoding Variational Inference for Aspect-based Opinion Summary","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Huy Le, Tai Hoang, Tho Quan","submitted_at":"2019-02-07T07:44:03Z","abstract_excerpt":"Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classification. However, unlike traditional topic modeling, in the context of aspect discovery it is often required some initial seed words, whose prior knowledge is not easy to be incorporated into LDA models. Moreover, LDA approaches rely on sampling met"},"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":"1902.02507","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-07T07:44:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"711e90f0ea7c9d3beb7da6e36b1ad5fe0c90c2000af2f068bc15d2c08f337049","abstract_canon_sha256":"a33a858765067b777ab2676149e7f431fc75d85567674022bcfa3db9683c5d3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:42.370098Z","signature_b64":"DdfTVLkpySv7NpjqjdrEc6+0aAzocnZQqfD2NpB6rQz4fIPda1UDVma74Py/jon9q1IUM9B2BFbfEnBvfoSpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"841ada0bd2c725920251ed5ce12ee7a4acb67c1f43adf352ca598e20cb185cc9","last_reissued_at":"2026-05-17T23:42:42.369436Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:42.369436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Autoencoding Variational Inference for Aspect-based Opinion Summary","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Huy Le, Tai Hoang, Tho Quan","submitted_at":"2019-02-07T07:44:03Z","abstract_excerpt":"Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classification. However, unlike traditional topic modeling, in the context of aspect discovery it is often required some initial seed words, whose prior knowledge is not easy to be incorporated into LDA models. Moreover, LDA approaches rely on sampling met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.02507","kind":"arxiv","version":3},"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":"1902.02507","created_at":"2026-05-17T23:42:42.369540+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.02507v3","created_at":"2026-05-17T23:42:42.369540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.02507","created_at":"2026-05-17T23:42:42.369540+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQNNUC6SY4SZ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQNNUC6SY4SZEASR","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQNNUC6S","created_at":"2026-05-18T12:33:27.125529+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/QQNNUC6SY4SZEASR5VOOCLXHUS","json":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS.json","graph_json":"https://pith.science/api/pith-number/QQNNUC6SY4SZEASR5VOOCLXHUS/graph.json","events_json":"https://pith.science/api/pith-number/QQNNUC6SY4SZEASR5VOOCLXHUS/events.json","paper":"https://pith.science/paper/QQNNUC6S"},"agent_actions":{"view_html":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS","download_json":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS.json","view_paper":"https://pith.science/paper/QQNNUC6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.02507&json=true","fetch_graph":"https://pith.science/api/pith-number/QQNNUC6SY4SZEASR5VOOCLXHUS/graph.json","fetch_events":"https://pith.science/api/pith-number/QQNNUC6SY4SZEASR5VOOCLXHUS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS/action/storage_attestation","attest_author":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS/action/author_attestation","sign_citation":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS/action/citation_signature","submit_replication":"https://pith.science/pith/QQNNUC6SY4SZEASR5VOOCLXHUS/action/replication_record"}},"created_at":"2026-05-17T23:42:42.369540+00:00","updated_at":"2026-05-17T23:42:42.369540+00:00"}