{"id":"W3093607642","doi":"10.18280/ria.340418","title":"Text Sentiment Classification Based on Feature Fusion","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China; National Science Foundation","keywords":"Softmax function; Computer science; Artificial intelligence; Convolutional neural network; Classifier (UML); Pattern recognition (psychology); Deep learning; Sentiment analysis; Artificial neural network; Feature (linguistics); Natural language processing; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004904722,0.0008830724,0.00074083,0.001490743,0.0003146281,0.0006558035,0.0004467758,0.0004879767,0.00211235],"category_scores_gemma":[0.001054729,0.0001743662,0.0007904235,0.0008644752,0.0002009904,0.001344736,0.0005895239,0.0005270909,0.001115421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004687821,"about_ca_system_score_gemma":0.0003672619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001775423,"about_ca_topic_score_gemma":0.001782238,"domain_scores_codex":[0.9996605,0.00002942308,0.0000317796,0.00009924165,0.0001179018,0.00006117221],"domain_scores_gemma":[0.9996517,0.00004811205,0.00004687976,0.00002613981,0.0002079157,0.00001925033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008035429,0.0002787053,0.009065054,0.0002097602,0.0001797263,0.000286495,0.0001342259,0.02376714,0.1005887,0.001905937,0.01249439,0.8502864],"study_design_scores_gemma":[0.00002902011,0.0001850128,0.009301232,0.00002877879,0.0001319073,0.0001562721,0.00008627344,0.9521011,0.03105467,0.002983154,0.003910024,0.00003246798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3848968,0.001572118,0.5871497,0.0006415371,0.0007525305,0.0003796395,0.001653267,0.006312811,0.01664152],"genre_scores_gemma":[0.9304815,0.0004490741,0.06190373,0.0001265695,0.0001912365,0.0001232551,0.001790279,0.00007904404,0.004855415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00211235,"threshold_uncertainty_score":0.007066548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05547955289847228,"score_gpt":0.2774688544247115,"score_spread":0.2219893015262392,"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."}}