{"id":"W2560450848","doi":"","title":"Empirical Evaluation of Automated Sentiment Analysis as a Decision Aid","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Sentiment analysis; Decision tree; Artificial intelligence; Data science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01127078,0.0007193222,0.0004424628,0.001889481,0.0005966631,0.001414697,0.0008985479,0.0008327548,0.003240208],"category_scores_gemma":[0.04865434,0.0001865231,0.0003529813,0.001254764,0.0004528694,0.001386715,0.000928204,0.0006019042,0.001150792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005923251,"about_ca_system_score_gemma":0.0005524234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001895832,"about_ca_topic_score_gemma":0.002221629,"domain_scores_codex":[0.9897178,0.007193451,0.000485336,0.0004808079,0.00188789,0.0002346914],"domain_scores_gemma":[0.9155972,0.06758466,0.002258818,0.002758523,0.01084935,0.0009514253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01683127,0.0123747,0.2431381,0.001847393,0.0010768,0.0005453769,0.002241314,0.0276919,0.03362523,0.003277311,0.02503509,0.6323156],"study_design_scores_gemma":[0.001612722,0.01041378,0.2589525,0.0002814829,0.001050935,0.000718222,0.002272885,0.6682068,0.03605351,0.003428976,0.01686842,0.0001398646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829019,0.0005876783,0.008813852,0.0003171209,0.0001151175,0.0002264756,0.0009248555,0.0002934685,0.005819545],"genre_scores_gemma":[0.9853205,0.0001615661,0.01107671,0.0000661145,0.00006970814,0.0001105881,0.001599471,0.00004047442,0.00155488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01127078,"threshold_uncertainty_score":0.05960625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08401158892468724,"score_gpt":0.3881525171092334,"score_spread":0.3041409281845461,"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."}}