{"id":"W4317387810","doi":"10.18280/mmep.090617","title":"Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Imam Abdulrahman Bin Faisal University; Saudi Aramco","keywords":"Sarcasm; Sentiment analysis; Computer science; Social media; Artificial intelligence; Natural language processing; Arabic; Customer satisfaction; Deep learning; Encoder; Recall; World Wide Web; Linguistics; Business; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003868654,0.0001164491,0.0001738225,0.0001733582,0.0001166669,0.00008255765,0.0001117741,0.00002589423,0.000004335948],"category_scores_gemma":[0.000004234287,0.0001168869,0.00003121013,0.0002110568,0.000008449841,0.0001482203,0.0002024605,0.0001520823,8.712411e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004146279,"about_ca_system_score_gemma":0.000006447213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001027656,"about_ca_topic_score_gemma":2.585416e-7,"domain_scores_codex":[0.9990758,0.0000163235,0.0002384491,0.0002711753,0.0001911228,0.0002070802],"domain_scores_gemma":[0.9997112,0.0000383031,0.00003658954,0.000143487,0.000009813236,0.00006065051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[8.229487e-7,0.00002642479,0.00002070609,0.00005870446,0.000009185586,0.000001371451,0.0008095009,0.9941596,0.0008243289,0.003600713,7.750323e-7,0.0004878082],"study_design_scores_gemma":[0.0001441562,0.00001754522,0.000004825446,0.00004922122,0.000009117849,0.00001790926,0.00002921129,0.9917265,0.0001600657,0.007682994,0.00002170958,0.0001367432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2402515,0.0001717564,0.7593378,0.00005208043,0.00003292002,0.00007486358,3.639564e-7,0.00004723368,0.00003148134],"genre_scores_gemma":[0.8878655,0.00001773027,0.1120218,0.00001419616,0.000008551842,0.00002188362,5.924783e-7,0.00001042328,0.00003924593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6476141,"threshold_uncertainty_score":0.4766509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150540110276414,"score_gpt":0.2207928954795215,"score_spread":0.1892874943767573,"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."}}