{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CK6TW5WYEXNYSC6PFTKXWIXMGY","short_pith_number":"pith:CK6TW5WY","schema_version":"1.0","canonical_sha256":"12bd3b76d825db890bcf2cd57b22ec36375e3fba08d6f2cd607df1d436194d93","source":{"kind":"arxiv","id":"2607.05259","version":1},"attestation_state":"computed","paper":{"title":"SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Adithya Galwatta, Lakshani Galwatta, Nisansa de Silva, Sarangi Aththanayake","submitted_at":"2026-07-06T16:05:26Z","abstract_excerpt":"Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications. In high-resource languages, this has been extended a step further, and instead of predicting sentiment at the sentence level, models have been developed to detect more fine-grained sentiments at aspect level. However, in order to conduct this fine-grained Aspect-based Sentiment Analysis (ABSA), datasets annotated with aspects and sentiments toward the said aspects is required. Such datasets are lacking for low-resources l"},"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":"2607.05259","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-07-06T16:05:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e7700541143b8436c580a30c203e1afb6050b0bc5053f5ce03d0961e9cb74a9e","abstract_canon_sha256":"e542bb2944362864962fc22ac61170e439ba771d855dc2e822c2b1d180794f87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T03:19:21.311036Z","signature_b64":"hfyQX99f2pEIPDLjI8s+wctX59UywbwOmsif4cwAjaOVcgMevhUJ5OlCeTT0hayACx+CQfrtdazFS2y9OuEcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12bd3b76d825db890bcf2cd57b22ec36375e3fba08d6f2cd607df1d436194d93","last_reissued_at":"2026-07-07T03:19:21.310591Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T03:19:21.310591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Adithya Galwatta, Lakshani Galwatta, Nisansa de Silva, Sarangi Aththanayake","submitted_at":"2026-07-06T16:05:26Z","abstract_excerpt":"Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications. In high-resource languages, this has been extended a step further, and instead of predicting sentiment at the sentence level, models have been developed to detect more fine-grained sentiments at aspect level. However, in order to conduct this fine-grained Aspect-based Sentiment Analysis (ABSA), datasets annotated with aspects and sentiments toward the said aspects is required. Such datasets are lacking for low-resources l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05259","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/2607.05259/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":"2607.05259","created_at":"2026-07-07T03:19:21.310661+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.05259v1","created_at":"2026-07-07T03:19:21.310661+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05259","created_at":"2026-07-07T03:19:21.310661+00:00"},{"alias_kind":"pith_short_12","alias_value":"CK6TW5WYEXNY","created_at":"2026-07-07T03:19:21.310661+00:00"},{"alias_kind":"pith_short_16","alias_value":"CK6TW5WYEXNYSC6P","created_at":"2026-07-07T03:19:21.310661+00:00"},{"alias_kind":"pith_short_8","alias_value":"CK6TW5WY","created_at":"2026-07-07T03:19:21.310661+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/CK6TW5WYEXNYSC6PFTKXWIXMGY","json":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY.json","graph_json":"https://pith.science/api/pith-number/CK6TW5WYEXNYSC6PFTKXWIXMGY/graph.json","events_json":"https://pith.science/api/pith-number/CK6TW5WYEXNYSC6PFTKXWIXMGY/events.json","paper":"https://pith.science/paper/CK6TW5WY"},"agent_actions":{"view_html":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY","download_json":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY.json","view_paper":"https://pith.science/paper/CK6TW5WY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.05259&json=true","fetch_graph":"https://pith.science/api/pith-number/CK6TW5WYEXNYSC6PFTKXWIXMGY/graph.json","fetch_events":"https://pith.science/api/pith-number/CK6TW5WYEXNYSC6PFTKXWIXMGY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY/action/storage_attestation","attest_author":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY/action/author_attestation","sign_citation":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY/action/citation_signature","submit_replication":"https://pith.science/pith/CK6TW5WYEXNYSC6PFTKXWIXMGY/action/replication_record"}},"created_at":"2026-07-07T03:19:21.310661+00:00","updated_at":"2026-07-07T03:19:21.310661+00:00"}