{"id":"W3147773549","doi":"10.18280/ria.350107","title":"Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wuhan Institute of Technology; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Artificial intelligence; Sentiment analysis; Convolutional neural network; Preprocessor; Artificial neural network; Support vector machine; Vectorization (mathematics); Word (group theory); Ensemble learning; Data pre-processing; Machine learning; Pattern recognition (psychology); Natural language processing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009446055,0.0001067751,0.0001607838,0.00030102,0.0004502151,0.0001197702,0.0003742418,0.00009094508,0.0002129137],"category_scores_gemma":[0.000150961,0.0001079575,0.0000796835,0.002321207,0.00009617576,0.00007039747,0.0001547536,0.000526084,0.0003800998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003855924,"about_ca_system_score_gemma":0.0000829378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001368395,"about_ca_topic_score_gemma":0.00001781975,"domain_scores_codex":[0.998257,0.0003146157,0.0002161219,0.0005733724,0.0002633219,0.0003755574],"domain_scores_gemma":[0.9980544,0.001006998,0.00005074419,0.0006319038,0.0001745058,0.00008138642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009938944,0.0001538553,0.003454506,0.000008657413,0.00003650127,0.0000365897,0.0003250251,0.8780369,0.0003537072,0.08944771,0.0007166525,0.02741994],"study_design_scores_gemma":[0.00001979121,0.0001767278,0.00143314,0.00003259983,0.00001440083,0.000008178463,0.0004433974,0.9667935,0.02243484,0.003551566,0.00497163,0.0001202783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3808509,0.0009181338,0.5734513,0.024522,0.0006581217,0.000246545,0.000001471087,0.0002412114,0.0191104],"genre_scores_gemma":[0.9916081,0.00004133083,0.003057301,0.0004481475,0.00007964933,0.00001620355,0.000006084191,0.000006192237,0.004736922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6107574,"threshold_uncertainty_score":0.4885541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1073994801737365,"score_gpt":0.3803853914787971,"score_spread":0.2729859113050606,"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."}}