{"id":"W4360989124","doi":"10.18280/ria.370101","title":"A Combined Approach of Sentimental Analysis Using Machine Learning Techniques","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Lexical analysis; Punctuation; Naive Bayes classifier; Support vector machine; Artificial intelligence; Machine learning; tf–idf; Random forest; Stop words; Feature selection; Information retrieval; Word (group theory); Feature (linguistics); Natural language processing; Data science; Data mining; Preprocessor","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":[0.0007407756,0.000152254,0.0003692819,0.0008361866,0.0001846387,0.00009522224,0.0006171746,0.00005582179,0.00009238692],"category_scores_gemma":[0.00003581305,0.0001501697,0.0003314611,0.005237216,0.00005911367,0.0002166521,0.0003263393,0.000139637,0.00007203861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002995965,"about_ca_system_score_gemma":0.0000179617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008506462,"about_ca_topic_score_gemma":0.000002148643,"domain_scores_codex":[0.9983306,0.0001012543,0.0005392861,0.0004529479,0.0002821899,0.0002937551],"domain_scores_gemma":[0.9989928,0.00009652462,0.0002494651,0.0005072027,0.00008790236,0.0000660788],"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":[0.00003043422,0.0007270427,0.07331174,0.0001612256,0.001732396,0.00003661757,0.008019633,0.7057168,0.1475034,0.02817767,0.0001843419,0.03439864],"study_design_scores_gemma":[0.00001933437,0.00004248912,0.00008503207,0.00001769764,0.00008930011,0.000002019504,0.0005162508,0.8305239,0.168269,0.0001379492,0.0001659991,0.0001310186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1140341,0.00009416352,0.884133,0.00007428094,0.00007970786,0.0001337716,0.000002179815,0.0002768879,0.001171864],"genre_scores_gemma":[0.9387144,0.00005624273,0.05998412,0.00001779523,0.00002643961,0.000008309169,0.00004844838,0.00001154854,0.001132692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8246803,"threshold_uncertainty_score":0.6123742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05717035176694336,"score_gpt":0.302642722098057,"score_spread":0.2454723703311137,"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."}}