{"id":"W2906928036","doi":"10.4000/books.aaccademia.4545","title":"Aspect-based Sentiment Analysis: X2Check at ABSITA 2018","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Task (project management); Set (abstract data type); Polarity (international relations); Computer science; Sentiment analysis; Artificial intelligence; Training set; Natural language processing; Information retrieval; Engineering; Programming language; Chemistry; Systems engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003281345,0.0005707624,0.0008002296,0.0008869732,0.0004462558,0.0001806899,0.002365125,0.0006844312,0.0007744166],"category_scores_gemma":[0.000005271461,0.0006318941,0.0009801281,0.0001075975,0.0002133533,0.0002444095,0.001740728,0.0005361275,0.0003347184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005510819,"about_ca_system_score_gemma":0.0001433195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009080195,"about_ca_topic_score_gemma":0.00003700687,"domain_scores_codex":[0.9968227,0.00007920165,0.0004439495,0.001314064,0.0008407041,0.0004994463],"domain_scores_gemma":[0.9970662,0.0001145841,0.0006960069,0.001632927,0.0001975222,0.0002927711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001728689,0.00009461745,0.0005741377,0.0001044756,0.01617802,0.0005135025,0.001640582,0.0005123619,0.0002680873,0.6561635,0.3208021,0.002975698],"study_design_scores_gemma":[0.0006536108,0.00006367572,0.00008401108,0.00009623608,0.002747468,0.000002289174,0.00001920707,0.01370804,0.001584218,0.0002710202,0.9799351,0.0008351915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0002396413,0.0002658663,0.02286852,0.0001312447,0.0003669986,0.0003011331,0.00004250409,0.0003153733,0.9754687],"genre_scores_gemma":[0.001897015,0.00004748232,0.003194718,0.0002650255,0.0002181604,8.644471e-7,0.000082375,0.00003467076,0.9942597],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6591329,"threshold_uncertainty_score":0.9996132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266538007875537,"score_gpt":0.2308946196921538,"score_spread":0.1982292396133984,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}