{"id":"W4410416953","doi":"10.54254/2753-8818/2025.22733","title":"Sentiment Analysis Applied on Tweets","year":2025,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sentiment analysis; Computer science; Information retrieval; Data science; Natural language processing; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006113219,0.00009802761,0.0001641583,0.0004066499,0.0002921845,0.0002586196,0.0006545904,0.00002358293,0.00005221451],"category_scores_gemma":[0.00003283921,0.00006514703,0.000072416,0.003237715,0.0007241447,0.0001343009,0.0003513106,0.0001032519,0.00002118191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002405442,"about_ca_system_score_gemma":0.00003443335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002212441,"about_ca_topic_score_gemma":3.583515e-7,"domain_scores_codex":[0.9986891,0.00002335596,0.0001437559,0.0004799422,0.0003989229,0.000264973],"domain_scores_gemma":[0.9994059,0.0001168976,0.00003112411,0.0003090295,0.00004183935,0.0000952355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005297363,0.00001810231,0.0004401614,0.000001012133,0.00003651325,7.585378e-7,0.0000509692,0.00002569299,0.001530658,0.9826396,0.0000380485,0.01521326],"study_design_scores_gemma":[0.0007113453,0.0001361215,0.08446085,0.00004408033,0.0003560847,0.000002617875,0.0001075382,0.5508318,0.06042152,0.3011566,0.001169016,0.0006024027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7458365,0.0004352151,0.1205691,0.008741412,0.0007714253,0.0002547171,0.00000108844,0.0002115411,0.123179],"genre_scores_gemma":[0.9966781,0.000006774485,0.002063203,0.0009427078,0.00001292186,0.000002414836,6.519161e-7,9.155066e-7,0.0002923384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6814829,"threshold_uncertainty_score":0.2668142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003777605526262453,"score_gpt":0.2581465589642757,"score_spread":0.2543689534380132,"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."}}