{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2EGUFMBUAFTQWXDGQOQZRZRIEN","short_pith_number":"pith:2EGUFMBU","canonical_record":{"source":{"id":"1805.08533","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-22T12:08:22Z","cross_cats_sorted":[],"title_canon_sha256":"77f6aed544df14a5ccab82bf92d3ef234304db8649221259a6c39ba92019cba5","abstract_canon_sha256":"d0d70ca4e35197780c8c1aae6b7e6ecdef78bc690a19d8a5a4e3d845601f92d4"},"schema_version":"1.0"},"canonical_sha256":"d10d42b03401670b5c6683a198e6282366791f906fff964f5668ff6e56e5068b","source":{"kind":"arxiv","id":"1805.08533","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08533","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08533v1","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08533","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"2EGUFMBUAFTQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2EGUFMBUAFTQWXDG","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2EGUFMBU","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2EGUFMBUAFTQWXDGQOQZRZRIEN","target":"record","payload":{"canonical_record":{"source":{"id":"1805.08533","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-22T12:08:22Z","cross_cats_sorted":[],"title_canon_sha256":"77f6aed544df14a5ccab82bf92d3ef234304db8649221259a6c39ba92019cba5","abstract_canon_sha256":"d0d70ca4e35197780c8c1aae6b7e6ecdef78bc690a19d8a5a4e3d845601f92d4"},"schema_version":"1.0"},"canonical_sha256":"d10d42b03401670b5c6683a198e6282366791f906fff964f5668ff6e56e5068b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:26.138507Z","signature_b64":"2RgrsiVUxZUV5YO7O4ySa+xj/Ao0Cv+Ce7sf0kOzGHgeG88q08kRWCAe4co3ql4GhyRPsjIRMc3ngv2KHD6nDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d10d42b03401670b5c6683a198e6282366791f906fff964f5668ff6e56e5068b","last_reissued_at":"2026-05-18T00:15:26.137913Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:26.137913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.08533","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wy36Xp5rJD2iixNWQ8uJr5q8vfCspAjS19RZaytsBl7JktSbbz1J1iMovgJiyLXnTVlxp5y9+2uI+6Oc8lKxAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T05:45:49.151786Z"},"content_sha256":"4c8c6e1d182bc4120e946d009caa12613fc17137f28afe8ae643335b92919bd8","schema_version":"1.0","event_id":"sha256:4c8c6e1d182bc4120e946d009caa12613fc17137f28afe8ae643335b92919bd8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2EGUFMBUAFTQWXDGQOQZRZRIEN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"AbdulMalik Alsalman, Hend Al-Khalifa, Nora Al-Twairesh, Yousef Al-Ohali","submitted_at":"2018-05-22T12:08:22Z","abstract_excerpt":"Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08533","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uWzMnawcLBOev+i2c2skYAXpla53bdCy4biAi+L8t4vmyDEtkuXSj3lYQ/crYaajJznSEwv0Nv1H73z60CwxAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T05:45:49.152231Z"},"content_sha256":"9fcf21d5f46d923534d53e827a7309cc2671a798b58e828dac0fd6177265dcbc","schema_version":"1.0","event_id":"sha256:9fcf21d5f46d923534d53e827a7309cc2671a798b58e828dac0fd6177265dcbc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN/bundle.json","state_url":"https://pith.science/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T05:45:49Z","links":{"resolver":"https://pith.science/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN","bundle":"https://pith.science/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN/bundle.json","state":"https://pith.science/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2EGUFMBUAFTQWXDGQOQZRZRIEN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2EGUFMBUAFTQWXDGQOQZRZRIEN","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":"d0d70ca4e35197780c8c1aae6b7e6ecdef78bc690a19d8a5a4e3d845601f92d4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-22T12:08:22Z","title_canon_sha256":"77f6aed544df14a5ccab82bf92d3ef234304db8649221259a6c39ba92019cba5"},"schema_version":"1.0","source":{"id":"1805.08533","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08533","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08533v1","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08533","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"2EGUFMBUAFTQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2EGUFMBUAFTQWXDG","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2EGUFMBU","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:9fcf21d5f46d923534d53e827a7309cc2671a798b58e828dac0fd6177265dcbc","target":"graph","created_at":"2026-05-18T00:15:26Z","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"},"paper":{"abstract_excerpt":"Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). T","authors_text":"AbdulMalik Alsalman, Hend Al-Khalifa, Nora Al-Twairesh, Yousef Al-Ohali","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-22T12:08:22Z","title":"Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08533","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":"sha256:4c8c6e1d182bc4120e946d009caa12613fc17137f28afe8ae643335b92919bd8","target":"record","created_at":"2026-05-18T00:15:26Z","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":"d0d70ca4e35197780c8c1aae6b7e6ecdef78bc690a19d8a5a4e3d845601f92d4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-22T12:08:22Z","title_canon_sha256":"77f6aed544df14a5ccab82bf92d3ef234304db8649221259a6c39ba92019cba5"},"schema_version":"1.0","source":{"id":"1805.08533","kind":"arxiv","version":1}},"canonical_sha256":"d10d42b03401670b5c6683a198e6282366791f906fff964f5668ff6e56e5068b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d10d42b03401670b5c6683a198e6282366791f906fff964f5668ff6e56e5068b","first_computed_at":"2026-05-18T00:15:26.137913Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:26.137913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2RgrsiVUxZUV5YO7O4ySa+xj/Ao0Cv+Ce7sf0kOzGHgeG88q08kRWCAe4co3ql4GhyRPsjIRMc3ngv2KHD6nDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:26.138507Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.08533","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c8c6e1d182bc4120e946d009caa12613fc17137f28afe8ae643335b92919bd8","sha256:9fcf21d5f46d923534d53e827a7309cc2671a798b58e828dac0fd6177265dcbc"],"state_sha256":"2196f3a7b53621eec4c3413087d4229505cde8f7ad4e1ae61f0dd9e1f41e527b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xyHT/HCEL9VARu+hCm83wwzKwtxp0Eotppfw7sogrrKYtlm1alJPZyy/xhkX1ceMInyTFbcRBeCzH27gZMFpBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T05:45:49.155946Z","bundle_sha256":"110763137eb6c475998dba94f934cbdfba471ef0358d382a36fd25f698a7d2d8"}}