{"public_id":"co_2cd148fe7e247152b84e79ce3db969e7","status":"active","merged_into_public_id":null,"resolved_public_id":"co_2cd148fe7e247152b84e79ce3db969e7","name":"sentiment classification","description":"A text classification task that assigns sentiment labels such as positive or negative.","aliases":["sentiment analysis classification"],"types":["task"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"origin_summary":{"object_type":"concept","status":"active","confidence":null,"origin_kinds":["extraction_create"],"contribution_count":1,"contribution_task_types":["extraction"],"contribution_statuses":["applied"],"verifier_verdict_count":0,"verifier_classes":[],"verifier_class_counts":{"system":0,"user_agent":0},"verdict_counts":{"approve":0,"reject":0},"verifier_state":"no_verdicts","basis":["kg_settlement_results.decision_payload.legacy_bridge","kg_entity_origin_refs","kg_assertion_proposals","contributions","verifications","concept.status"],"limits":["ledger provenance is aggregated; raw contribution and verifier audit rows are not expanded","entity matching uses settlement bridge refs and edge commands"]},"papers":[{"corpus_id":32411082,"title":"Data properties and the performance of sentiment classification for electronic commerce applications","citation_count":55,"url":"https://sah.borca.ai/papers/32411082"}],"claims":[{"public_id":"cl_5613418d160a2cfb9a2d595f9ba0d710","text":"Sentiment classification techniques are sensitive to dataset size, document length, and the subjectivity of training and test data.","corpus_id":32411082,"url":"https://sah.borca.ai/claims/cl_5613418d160a2cfb9a2d595f9ba0d710"}],"related_concepts":[],"resolved_url":"https://sah.borca.ai/concepts/co_2cd148fe7e247152b84e79ce3db969e7","url":"https://sah.borca.ai/concepts/co_2cd148fe7e247152b84e79ce3db969e7"}