{"id":"W3043133489","doi":"10.30865/mib.v4i3.2140","title":"Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes","year":2020,"lang":"en","type":"article","venue":"JURNAL MEDIA INFORMATIKA BUDIDARMA","topic":"Linguistics and Language Analysis","field":"Arts and Humanities","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Naive Bayes classifier; Social media; Quarter (Canadian coin); Indonesian; Computer science; Microblogging; Sentiment analysis; Artificial intelligence; Psychology; Advertising; World Wide Web; Geography; Business; Support vector machine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002285488,0.001299332,0.0009635873,0.002743245,0.001344205,0.002840316,0.0006760635,0.0008884849,0.005482763],"category_scores_gemma":[0.008238315,0.0003147448,0.00138414,0.001776951,0.0004795865,0.001667978,0.0006021188,0.001062517,0.002816375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008856332,"about_ca_system_score_gemma":0.000979829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01111578,"about_ca_topic_score_gemma":0.009062648,"domain_scores_codex":[0.9979879,0.0003225958,0.000282302,0.0004781224,0.000698178,0.0002308406],"domain_scores_gemma":[0.9965852,0.002146176,0.0002406522,0.0001293843,0.0007882348,0.0001102408],"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":[0.002214324,0.000749857,0.369265,0.001360983,0.0008309692,0.001233347,0.003192765,0.01715969,0.006490072,0.003545796,0.0308032,0.5631539],"study_design_scores_gemma":[0.0001315194,0.001163432,0.4845204,0.001212579,0.001042504,0.002907707,0.01392624,0.4371098,0.01025604,0.009180391,0.03822187,0.0003274615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8860117,0.008080702,0.05137672,0.00318297,0.001689638,0.0007935319,0.006448189,0.001122367,0.04129424],"genre_scores_gemma":[0.974386,0.001354782,0.01103053,0.0002510256,0.0002857618,0.0002116854,0.003973354,0.00005403459,0.008452929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01111578,"threshold_uncertainty_score":0.02210218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02668459578563655,"score_gpt":0.2278735088456125,"score_spread":0.201188913059976,"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."}}