{"id":"W2945245827","doi":"10.1109/spin.2019.8711636","title":"Accuracy of Convolution Neural Networks for Classifying Sentiments on Movie Reviews","year":2019,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science; Sentence; Word embedding; Convolutional neural network; Artificial intelligence; Kernel (algebra); Embedding; Word (group theory); Convolution (computer science); Natural language processing; Task (project management); Artificial neural network; Pattern recognition (psychology); Machine learning; Mathematics","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":[0.00328732,0.001340322,0.0005847377,0.001580962,0.0003113562,0.001205355,0.0005157148,0.0009780243,0.001826525],"category_scores_gemma":[0.01076907,0.0002345201,0.0006209082,0.0008177594,0.0002920768,0.001653496,0.0006357211,0.0006965921,0.001476581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008099233,"about_ca_system_score_gemma":0.0004753769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009818834,"about_ca_topic_score_gemma":0.01127411,"domain_scores_codex":[0.9986688,0.000333935,0.0001560842,0.0003012674,0.0003694734,0.0001705794],"domain_scores_gemma":[0.995922,0.002071937,0.0003778689,0.0004522186,0.001010955,0.0001649898],"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":[0.002510431,0.0004723892,0.1938879,0.0005849589,0.0009826258,0.0002599365,0.0003462381,0.1364661,0.0274778,0.001815891,0.02897666,0.6062192],"study_design_scores_gemma":[0.00002452028,0.0001854298,0.03980505,0.00005191378,0.0001061927,0.00009221902,0.0001589431,0.9444504,0.01250542,0.001041233,0.001546345,0.00003236998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508644,0.002609347,0.02940761,0.0007559941,0.0003821527,0.00005612138,0.002598943,0.001835689,0.01148973],"genre_scores_gemma":[0.9851888,0.0004440383,0.007962365,0.00006655143,0.00006598794,0.00001460068,0.003700727,0.00006607604,0.002490896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009818834,"threshold_uncertainty_score":0.01952332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06830139006968847,"score_gpt":0.3136145710172231,"score_spread":0.2453131809475346,"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."}}