{"id":"W4319599375","doi":"10.3390/computers12020037","title":"Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research","year":2023,"lang":"en","type":"review","venue":"Computers","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West","funders":"","keywords":"Competitor analysis; Sentiment analysis; Competitive intelligence; Connotation; Competition (biology); Process (computing); Statement (logic); Marketing; Computer science; Data science; Business; Artificial intelligence; Political science; Linguistics","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":[0.006494294,0.0001843423,0.001310045,0.0006805308,0.0002443504,0.00021104,0.0004400694,0.0001568281,0.00002092873],"category_scores_gemma":[0.001225538,0.0001755011,0.0003177345,0.004733295,0.0005370094,0.00006265541,0.0002641063,0.0004889275,0.0001693278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537275,"about_ca_system_score_gemma":0.0003858895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004646225,"about_ca_topic_score_gemma":0.00133297,"domain_scores_codex":[0.995519,0.002261738,0.0005993986,0.000480587,0.0006612933,0.0004780154],"domain_scores_gemma":[0.9964827,0.002892271,0.0001495933,0.0001921729,0.0001133343,0.0001699817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[6.505654e-7,0.00002476391,0.000004340349,0.006139017,0.0001682785,0.00003090483,0.001488768,3.693182e-7,2.796032e-10,0.01612962,0.0006148345,0.9753985],"study_design_scores_gemma":[0.000006155246,0.00001451448,0.00000610789,0.05761796,0.0003986966,2.935227e-7,0.001194687,0.00001562641,4.961034e-9,0.001432332,0.9391177,0.0001958735],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001705757,0.9889759,0.00004575701,0.0003994089,0.0004027773,0.0008739375,0.00001107783,0.0000661068,0.009223321],"genre_scores_gemma":[0.00002580192,0.9991537,0.00006794003,0.00004999727,0.000246199,0.00008428634,0.00002412325,0.00001664717,0.0003313304],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9752026,"threshold_uncertainty_score":0.7156727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3576481061405088,"score_gpt":0.5144941713526183,"score_spread":0.1568460652121095,"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."}}