{"id":"W4387377888","doi":"10.59934/jaiea.v3i1.274","title":"Sentiment Analysis Using Text Mining Techniques On Social Media Using the Support Vector Machine Method Case Study Seagames 2023 Football Final","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Sentiment analysis; Social media; Support vector machine; Football; Computer science; Event (particle physics); Data science; Data mining; Artificial intelligence; 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":[0.00164979,0.0001982748,0.0003220045,0.0007476188,0.0005419475,0.0002837486,0.0005777127,0.0000626859,0.000008731306],"category_scores_gemma":[0.0001048152,0.0001631631,0.0001561838,0.002548421,0.00004720701,0.0002025915,0.000189326,0.0003731477,0.000008615913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006799971,"about_ca_system_score_gemma":0.0000706092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001179328,"about_ca_topic_score_gemma":0.0000322027,"domain_scores_codex":[0.9982831,0.0000980084,0.0006531717,0.0003305233,0.0003607955,0.0002744404],"domain_scores_gemma":[0.9983754,0.0006122029,0.0003376011,0.0004075056,0.0001608279,0.0001065229],"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":[0.00002708109,0.0006158793,0.0007385628,0.00003961702,0.001123724,0.0003354824,0.01666431,0.3713369,0.01132865,0.007782256,0.0003653452,0.5896422],"study_design_scores_gemma":[0.0000434943,0.0001334956,0.00065715,0.00002131861,0.000484294,0.0005861149,0.004629952,0.9892477,0.002535703,0.0002142715,0.001191778,0.0002547324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2095396,0.00003244018,0.7896329,0.0003702728,0.00008493649,0.0002071175,0.00001500886,0.0001076599,0.00001010443],"genre_scores_gemma":[0.8800646,0.00001621041,0.1195111,0.00002617004,0.0002944858,0.00004643988,0.000007005277,0.00001884489,0.00001513315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.670525,"threshold_uncertainty_score":0.6653597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1084257832543152,"score_gpt":0.3855944081663263,"score_spread":0.2771686249120111,"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."}}