{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001689419,0.0001779323,0.0001532892,0.0001226405,0.0001884668,0.0001978105,0.001047109,0.0001181543,0.0001587512],"category_scores_gemma":[0.000129172,0.00016297,0.00009661914,0.0008736401,0.00006498042,0.0002554056,0.0001486684,0.0002405241,0.001937593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005953669,"about_ca_system_score_gemma":0.00004000014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001701587,"about_ca_topic_score_gemma":4.593126e-7,"domain_scores_codex":[0.9984682,0.00004989918,0.0002919049,0.000633875,0.0002947726,0.0002613348],"domain_scores_gemma":[0.998677,0.0001053589,0.0001510477,0.000861921,0.00008341493,0.000121215],"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":[0.000051305,0.0004487031,0.0004987963,0.00007667323,0.00001424238,0.00001906031,0.001683056,0.0226713,0.06587075,0.3333889,0.0366993,0.5385779],"study_design_scores_gemma":[0.0000399682,0.0001553951,0.0002071033,0.00002690238,0.000003226284,0.000001184502,0.0002844988,0.8219402,0.1296577,0.0009764728,0.04653573,0.0001716729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001846875,0.0000720339,0.9037543,0.08533414,0.0002267095,0.0003004663,0.000002457392,0.0007751184,0.007687929],"genre_scores_gemma":[0.9843987,0.00003309837,0.01225259,0.002008933,0.00006140787,0.00003944445,0.000011636,0.00001167801,0.001182581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9825518,"threshold_uncertainty_score":0.9988395,"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."}}