{"id":"W4406194178","doi":"10.1016/j.eswa.2024.126287","title":"Public opinion prediction on social media by using machine learning methods","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Humanities and Social Science Fund of Ministry of Education of China; Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Public opinion; Social media; Artificial intelligence; Machine learning; Sentiment analysis; Support vector machine; Data science; World Wide Web; Political science","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":[0.000472082,0.000140832,0.0002037839,0.0002460749,0.0006671695,0.0002903039,0.0003961572,0.00007338799,0.000007551048],"category_scores_gemma":[0.00002708521,0.0001185432,0.00005585991,0.001001771,0.00003152626,0.0002405456,0.00007917726,0.00014607,0.00001143093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054776,"about_ca_system_score_gemma":0.00006083008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007040926,"about_ca_topic_score_gemma":0.000002387349,"domain_scores_codex":[0.9985655,0.0002335742,0.0003033991,0.0004274724,0.0002683103,0.0002017461],"domain_scores_gemma":[0.9991188,0.0001941442,0.0001740492,0.0003403106,0.0001091856,0.00006350225],"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":[0.00003635073,0.0008017511,0.008346601,0.0001258655,0.0008394654,0.000001149596,0.007943499,0.003815262,0.03513061,0.6001506,0.05902772,0.2837811],"study_design_scores_gemma":[0.0003758997,0.00002447048,0.0001630382,0.00006380294,0.0000124446,0.000003326546,0.0004767019,0.5738646,0.000874312,0.00007812422,0.4238735,0.0001897593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000168905,0.001668292,0.9948662,0.0009490983,0.0004140892,0.0002997363,0.000005340243,0.0002173986,0.00141096],"genre_scores_gemma":[0.7526718,0.0002997324,0.2395141,0.0006306583,0.001869409,0.002587938,0.0006202658,0.00006843821,0.001737685],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7553521,"threshold_uncertainty_score":0.5131395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07570998929517869,"score_gpt":0.3565352943096752,"score_spread":0.2808253050144965,"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."}}