{"id":"W4379385814","doi":"10.14569/ijacsa.2023.0140505","title":"An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Overfitting; Computer science; Support vector machine; WordNet; Sentiment analysis; Artificial intelligence; Machine learning; Benchmark (surveying); Identification (biology); Kernel (algebra); Task (project management); Artificial neural network","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":[0.00103549,0.00009199802,0.000179317,0.001458759,0.0001505398,0.0003927028,0.001453885,0.00002087852,0.000001365415],"category_scores_gemma":[0.00001773293,0.00008699919,0.0001053382,0.00261335,0.00006873976,0.001440714,0.0001491809,0.00006483452,0.000003840154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009739993,"about_ca_system_score_gemma":0.0001207627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002297013,"about_ca_topic_score_gemma":0.000005174578,"domain_scores_codex":[0.9982265,0.00001487902,0.0005264177,0.0003872599,0.0006565987,0.0001883362],"domain_scores_gemma":[0.9981781,0.00007378165,0.0003727002,0.0003020289,0.0009721561,0.0001012641],"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":[0.00001009899,0.0001304464,0.0006225847,0.00000267122,0.0001316409,0.000001790002,0.000837832,0.5919978,0.05680695,0.03469144,0.00005544433,0.3147113],"study_design_scores_gemma":[0.0003082588,0.00003645187,0.008360171,0.000008621902,0.00002471654,0.000003799634,0.00005782891,0.9783363,0.005843721,0.006712588,0.000213176,0.00009438449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0841971,0.00002060276,0.9146081,0.0007920805,0.0001869452,0.0001508237,0.000004122076,0.00002697843,0.00001320814],"genre_scores_gemma":[0.8967459,0.000056087,0.1028244,0.0001437359,0.0001368876,0.00004975294,0.000008949445,0.000003686381,0.00003063531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8125488,"threshold_uncertainty_score":0.3786843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01869574222535429,"score_gpt":0.3463898390964029,"score_spread":0.3276940968710486,"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."}}