{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003684825,0.001731835,0.0008349348,0.002949166,0.0008454242,0.002473864,0.001384127,0.001032637,0.02722303],"category_scores_gemma":[0.008749193,0.0007103287,0.0008720846,0.002137982,0.0003210592,0.003548135,0.002809983,0.001412299,0.02324712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137889,"about_ca_system_score_gemma":0.001102062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004260083,"about_ca_topic_score_gemma":0.004276602,"domain_scores_codex":[0.9973143,0.0005536421,0.0001691377,0.0005214053,0.001270852,0.0001706207],"domain_scores_gemma":[0.996307,0.0008392085,0.0001849784,0.0007455332,0.001663092,0.0002602738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001388308,0.0004002649,0.006559682,0.0006795622,0.0001553544,0.0002505923,0.0005718415,0.001526667,0.02464163,0.002354316,0.5575876,0.4038841],"study_design_scores_gemma":[0.00105925,0.001298459,0.03966428,0.0003666488,0.0002414071,0.0009894106,0.0007692695,0.1422403,0.09168319,0.00966767,0.7117306,0.0002894279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.2044883,0.007342575,0.139436,0.003404759,0.005712882,0.002083909,0.09333145,0.4476505,0.09654985],"genre_scores_gemma":[0.2970295,0.001680424,0.2547994,0.001312459,0.001074978,0.001390255,0.3187228,0.03308833,0.09090197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02722303,"threshold_uncertainty_score":0.09107006,"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."}}