{"id":"W2960505694","doi":"10.5120/ijca2019919167","title":"Twitter Texts’ Quality Classification using Data Mining and Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Quality (philosophy); Artificial neural network; Artificial intelligence; Data mining; Data science; Information retrieval","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":[0.001755511,0.0007525127,0.0005743437,0.004776335,0.000544604,0.001667866,0.0007952906,0.0009880498,0.0008127057],"category_scores_gemma":[0.008617682,0.0001978515,0.0007652624,0.002289806,0.000361862,0.001845451,0.0008461177,0.0008166765,0.0006052381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001336935,"about_ca_system_score_gemma":0.0005059097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004420951,"about_ca_topic_score_gemma":0.004428966,"domain_scores_codex":[0.9984944,0.0003031927,0.0002433967,0.0002665583,0.0005665758,0.000125867],"domain_scores_gemma":[0.995033,0.001844828,0.001143947,0.0002637552,0.001568924,0.0001454558],"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":[0.001625998,0.001657306,0.3744721,0.0007924231,0.0005364483,0.0004966694,0.0006611342,0.07540619,0.01186951,0.002456906,0.009596058,0.5204293],"study_design_scores_gemma":[0.00003154001,0.0002348958,0.0652232,0.0001077213,0.0001053938,0.0001126991,0.0006463116,0.9186327,0.009245048,0.002920584,0.002701035,0.00003875538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8509521,0.00114645,0.1321821,0.002384403,0.0002324143,0.0007146424,0.004748345,0.001213258,0.006426218],"genre_scores_gemma":[0.9553284,0.000300987,0.03804277,0.0001129631,0.0001503427,0.0002208468,0.004259309,0.00002773252,0.001556535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004776335,"threshold_uncertainty_score":0.009700179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106749870591907,"score_gpt":0.3798573457806176,"score_spread":0.2691823587214269,"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."}}