{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3D2DJGM3P7NJRKUI5HUQL6DH7M","short_pith_number":"pith:3D2DJGM3","schema_version":"1.0","canonical_sha256":"d8f434999b7fda98aa88e9e905f867fb38d8a4265ad3d2505ec4384d7d554d77","source":{"kind":"arxiv","id":"2305.15005","version":1},"attestation_state":"computed","paper":{"title":"Sentiment Analysis in the Era of Large Language Models: A Reality Check","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bing Liu, Lidong Bing, Sinno Jialin Pan, Wenxuan Zhang, Yue Deng","submitted_at":"2023-05-24T10:45:25Z","abstract_excerpt":"Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen considerable interest from both academia and industry. With the advent of large language models (LLMs) such as ChatGPT, there is a great potential for their employment on SA problems. However, the extent to which existing LLMs can be leveraged for different sentiment analysis tasks remains unclear. This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analys"},"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":"2305.15005","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T10:45:25Z","cross_cats_sorted":[],"title_canon_sha256":"f4deb51922debc59076692b85f8ff7277cdbf5f4e2c610b4ceec1c408f7bfb36","abstract_canon_sha256":"765f9bd118dcf1b7aef28863dda0b12d55d0eba7d0daa4289740af8486696602"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:30.070972Z","signature_b64":"wtnMd+l4O//F5BRZIYP2jSwn1EaSZ1HZNEAeGSCm2WRv27QZ9gk5E9jYGmmCIFsRhuH53oIFNnTLHVyLLhqPDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8f434999b7fda98aa88e9e905f867fb38d8a4265ad3d2505ec4384d7d554d77","last_reissued_at":"2026-07-05T06:13:30.070546Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:30.070546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sentiment Analysis in the Era of Large Language Models: A Reality Check","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bing Liu, Lidong Bing, Sinno Jialin Pan, Wenxuan Zhang, Yue Deng","submitted_at":"2023-05-24T10:45:25Z","abstract_excerpt":"Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen considerable interest from both academia and industry. With the advent of large language models (LLMs) such as ChatGPT, there is a great potential for their employment on SA problems. However, the extent to which existing LLMs can be leveraged for different sentiment analysis tasks remains unclear. This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analys"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15005","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/2305.15005/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":"2305.15005","created_at":"2026-07-05T06:13:30.070602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15005v1","created_at":"2026-07-05T06:13:30.070602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15005","created_at":"2026-07-05T06:13:30.070602+00:00"},{"alias_kind":"pith_short_12","alias_value":"3D2DJGM3P7NJ","created_at":"2026-07-05T06:13:30.070602+00:00"},{"alias_kind":"pith_short_16","alias_value":"3D2DJGM3P7NJRKUI","created_at":"2026-07-05T06:13:30.070602+00:00"},{"alias_kind":"pith_short_8","alias_value":"3D2DJGM3","created_at":"2026-07-05T06:13:30.070602+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2310.11113","citing_title":"Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models","ref_index":74,"is_internal_anchor":false},{"citing_arxiv_id":"2406.04244","citing_title":"Benchmark Data Contamination of Large Language Models: A Survey","ref_index":180,"is_internal_anchor":false},{"citing_arxiv_id":"2309.08532","citing_title":"EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers","ref_index":126,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06423","citing_title":"Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models","ref_index":54,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18955","citing_title":"Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11312","citing_title":"Network Effects and Agreement Drift in LLM Debates","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07369","citing_title":"The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15547","citing_title":"Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17569","citing_title":"MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M","json":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M.json","graph_json":"https://pith.science/api/pith-number/3D2DJGM3P7NJRKUI5HUQL6DH7M/graph.json","events_json":"https://pith.science/api/pith-number/3D2DJGM3P7NJRKUI5HUQL6DH7M/events.json","paper":"https://pith.science/paper/3D2DJGM3"},"agent_actions":{"view_html":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M","download_json":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M.json","view_paper":"https://pith.science/paper/3D2DJGM3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15005&json=true","fetch_graph":"https://pith.science/api/pith-number/3D2DJGM3P7NJRKUI5HUQL6DH7M/graph.json","fetch_events":"https://pith.science/api/pith-number/3D2DJGM3P7NJRKUI5HUQL6DH7M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M/action/storage_attestation","attest_author":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M/action/author_attestation","sign_citation":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M/action/citation_signature","submit_replication":"https://pith.science/pith/3D2DJGM3P7NJRKUI5HUQL6DH7M/action/replication_record"}},"created_at":"2026-07-05T06:13:30.070602+00:00","updated_at":"2026-07-05T06:13:30.070602+00:00"}