{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:757BGFBOLAZBHGWQMLZSK4MCEM","short_pith_number":"pith:757BGFBO","schema_version":"1.0","canonical_sha256":"ff7e13142e5832139ad062f32571822323486194fc2f94e4459cb3d37195a7de","source":{"kind":"arxiv","id":"1704.05228","version":3},"attestation_state":"computed","paper":{"title":"Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mathias Kraus, Stefan Feuerriegel","submitted_at":"2017-04-18T08:24:20Z","abstract_excerpt":"Prominent applications of sentiment analysis are countless, covering areas such as marketing, customer service and communication. The conventional bag-of-words approach for measuring sentiment merely counts term frequencies; however, it neglects the position of the terms within the discourse. As a remedy, we develop a discourse-aware method that builds upon the discourse structure of documents. For this purpose, we utilize rhetorical structure theory to label (sub-)clauses according to their hierarchical relationships and then assign polarity scores to individual leaves. To learn from the resu"},"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":"1704.05228","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-18T08:24:20Z","cross_cats_sorted":[],"title_canon_sha256":"8a0eeca7f94fc8545b7c23c6e6362e9457c5d16a66732669a7bb50a21e7a2c53","abstract_canon_sha256":"fd663539ad79a4764b3e6ee354e0a0cfb5ab6082bc0f443b2079e09f8fec931c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:05.247811Z","signature_b64":"FBoacxSsumqxFEu2xMulFbNCelraFK9JrJqHEeYV0cU+NEA5VDlUk2of6SfEEX9XqzExcF9lrOHI1P8rZbpnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff7e13142e5832139ad062f32571822323486194fc2f94e4459cb3d37195a7de","last_reissued_at":"2026-05-18T00:04:05.247184Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:05.247184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mathias Kraus, Stefan Feuerriegel","submitted_at":"2017-04-18T08:24:20Z","abstract_excerpt":"Prominent applications of sentiment analysis are countless, covering areas such as marketing, customer service and communication. The conventional bag-of-words approach for measuring sentiment merely counts term frequencies; however, it neglects the position of the terms within the discourse. As a remedy, we develop a discourse-aware method that builds upon the discourse structure of documents. For this purpose, we utilize rhetorical structure theory to label (sub-)clauses according to their hierarchical relationships and then assign polarity scores to individual leaves. To learn from the resu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.05228","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":"1704.05228","created_at":"2026-05-18T00:04:05.247285+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.05228v3","created_at":"2026-05-18T00:04:05.247285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.05228","created_at":"2026-05-18T00:04:05.247285+00:00"},{"alias_kind":"pith_short_12","alias_value":"757BGFBOLAZB","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"757BGFBOLAZBHGWQ","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"757BGFBO","created_at":"2026-05-18T12:31:03.183658+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/757BGFBOLAZBHGWQMLZSK4MCEM","json":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM.json","graph_json":"https://pith.science/api/pith-number/757BGFBOLAZBHGWQMLZSK4MCEM/graph.json","events_json":"https://pith.science/api/pith-number/757BGFBOLAZBHGWQMLZSK4MCEM/events.json","paper":"https://pith.science/paper/757BGFBO"},"agent_actions":{"view_html":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM","download_json":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM.json","view_paper":"https://pith.science/paper/757BGFBO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.05228&json=true","fetch_graph":"https://pith.science/api/pith-number/757BGFBOLAZBHGWQMLZSK4MCEM/graph.json","fetch_events":"https://pith.science/api/pith-number/757BGFBOLAZBHGWQMLZSK4MCEM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM/action/storage_attestation","attest_author":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM/action/author_attestation","sign_citation":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM/action/citation_signature","submit_replication":"https://pith.science/pith/757BGFBOLAZBHGWQMLZSK4MCEM/action/replication_record"}},"created_at":"2026-05-18T00:04:05.247285+00:00","updated_at":"2026-05-18T00:04:05.247285+00:00"}