{"id":"W4391102777","doi":"10.32985/ijeces.15.1.7","title":"A Survey of Sentiment Analysis and Sarcasm Detection","year":2024,"lang":"en","type":"article","venue":"International journal of electrical and computer engineering systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sarcasm; Sentiment analysis; Computer science; Data science; Social media; Resource (disambiguation); Artificial intelligence; Field (mathematics); World Wide Web; Linguistics; Irony","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":[0.0004569457,0.00007534146,0.0002177338,0.000729201,0.00001223284,0.0002689198,0.0002067817,0.00002693406,0.000001132737],"category_scores_gemma":[0.00001381347,0.00006037646,0.00009573118,0.000655557,0.000006665288,0.0001816402,0.00006291812,0.00009285228,4.57648e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002928527,"about_ca_system_score_gemma":0.00001546788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006789286,"about_ca_topic_score_gemma":0.000001714338,"domain_scores_codex":[0.9990485,0.00004053431,0.0003822509,0.0001301527,0.0003191188,0.00007946675],"domain_scores_gemma":[0.9993877,0.0001794052,0.0001124007,0.00005798648,0.0002079269,0.00005453668],"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":[0.00008005173,0.0003237189,0.09373175,0.0002623163,0.02714422,0.0004757332,0.001545894,0.1619836,0.01523897,0.0318239,0.0006047502,0.6667851],"study_design_scores_gemma":[0.00009600545,0.00007964794,0.04756632,0.0000574587,0.00006149711,0.0001110014,0.000001064734,0.9512349,0.00047025,0.00001264904,0.0002522379,0.00005691319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1308497,0.003049269,0.8651713,0.00004599797,0.0008465804,0.00001917752,9.685687e-7,0.00001368427,0.000003378168],"genre_scores_gemma":[0.9977108,0.00008261047,0.002020257,0.000006214033,0.0001615005,5.55048e-7,8.958455e-7,0.000003163273,0.00001401563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8668611,"threshold_uncertainty_score":0.25932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009508061209681142,"score_gpt":0.2385907849055376,"score_spread":0.2290827236958564,"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."}}