{"id":"W3092005458","doi":"10.3390/iot1020014","title":"Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IoT","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Artificial intelligence; Computer science; Sentiment analysis; Natural language processing; WordNet; Lexical analysis; Support vector machine; Naive Bayes classifier; Lexicon; Machine learning; Parsing; Stop words; Random forest; Preprocessor","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":[0.000300033,0.0001290361,0.0003050643,0.0002937372,0.00009139384,0.0001242771,0.0008571306,0.0000198022,0.0004433012],"category_scores_gemma":[0.00001804107,0.0001113253,0.0001647034,0.001424897,0.00001603606,0.0001354634,0.0006737954,0.0001471636,0.00003870624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003024538,"about_ca_system_score_gemma":0.00001868693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007651096,"about_ca_topic_score_gemma":0.00001031831,"domain_scores_codex":[0.9984462,0.00008942847,0.0003429197,0.0004803714,0.0004531855,0.0001878566],"domain_scores_gemma":[0.998939,0.00004981904,0.0002317076,0.0006448805,0.0000355811,0.00009901725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008249873,0.0006367834,0.6074892,0.00006965504,0.008029321,0.00009319435,0.008202652,0.307777,0.02433783,0.002011534,0.009787636,0.03148266],"study_design_scores_gemma":[0.0002093516,0.00003847906,0.001798479,0.00001785309,0.0002768619,3.439637e-7,0.00004839954,0.9875186,0.003707739,0.00001081209,0.006243801,0.0001292496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4751092,0.0005161462,0.5136535,0.008749796,0.0003378977,0.000182981,0.00001136247,0.000121159,0.001318002],"genre_scores_gemma":[0.9738867,0.00000706645,0.02426129,0.001263931,0.0001197168,7.179114e-7,0.00003212004,0.000008385278,0.0004200631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6797416,"threshold_uncertainty_score":0.4853837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1497165917975568,"score_gpt":0.3427472063976341,"score_spread":0.1930306146000772,"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."}}