{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004846989,0.000556174,0.0003649658,0.002456553,0.000425718,0.0006217817,0.0002446831,0.0003304406,0.00208569],"category_scores_gemma":[0.001261356,0.0000729852,0.000523135,0.001686987,0.0001432534,0.0004377102,0.0003768221,0.0003330244,0.001822846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003967106,"about_ca_system_score_gemma":0.0003170932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004076777,"about_ca_topic_score_gemma":0.006146947,"domain_scores_codex":[0.9994973,0.00008072203,0.00007208368,0.00008219836,0.0001849315,0.00008279613],"domain_scores_gemma":[0.9993945,0.0001395328,0.00008570868,0.00004046299,0.0002960619,0.00004370046],"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":[0.00169667,0.001046373,0.2674533,0.002018574,0.000432987,0.002775233,0.001776776,0.01169308,0.07307068,0.002796469,0.1082825,0.5269573],"study_design_scores_gemma":[0.00008354904,0.0008788922,0.6513823,0.0002399382,0.0001824472,0.001346031,0.004965,0.1937276,0.04721281,0.002176547,0.09763316,0.0001717054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8643104,0.0005441702,0.01793122,0.001097044,0.0004927376,0.0007405423,0.09180865,0.00219709,0.02087817],"genre_scores_gemma":[0.8676392,0.0005031377,0.02919312,0.0001677382,0.0002502057,0.0007877855,0.09221198,0.0001050149,0.009141814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004076777,"threshold_uncertainty_score":0.008106112,"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."}}