{"id":"W4400488140","doi":"10.1109/access.2024.3426329","title":"Uncovering Concerns of Citizens Through Machine Learning and Social Network Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Artificial intelligence; Machine learning; Cluster analysis; Topic model; Software deployment; Empowerment; Data science; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002189233,0.0005029758,0.0003524077,0.004644038,0.0007171684,0.001477249,0.0002843699,0.0005296102,0.001306822],"category_scores_gemma":[0.006675142,0.0001227638,0.0004277187,0.002644707,0.0003802148,0.001797471,0.0008086906,0.0006306057,0.0007339421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000932158,"about_ca_system_score_gemma":0.0006299774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006190602,"about_ca_topic_score_gemma":0.0116365,"domain_scores_codex":[0.9986171,0.0005975534,0.0001220231,0.0002159365,0.0003213326,0.0001261785],"domain_scores_gemma":[0.9934069,0.003642814,0.001360013,0.0002469854,0.001168691,0.0001745629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004154031,0.000320519,0.549365,0.001088101,0.0002122181,0.0008200065,0.02185316,0.004258049,0.02424947,0.005770856,0.01981403,0.3718332],"study_design_scores_gemma":[0.00003056706,0.0002870166,0.6675919,0.0004218384,0.0001756465,0.0004641773,0.05801873,0.1937736,0.01001109,0.01117904,0.05788507,0.0001613851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952735,0.0006472912,0.02565957,0.002055623,0.000116259,0.0003322751,0.005914294,0.0002147189,0.01232478],"genre_scores_gemma":[0.9762231,0.0004850067,0.01671955,0.000244142,0.0001458392,0.0002179234,0.003528134,0.00003773868,0.002398582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006190602,"threshold_uncertainty_score":0.01230913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607952095741991,"score_gpt":0.3371834822340259,"score_spread":0.301103961276606,"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."}}