{"id":"W3217030887","doi":"10.18280/isi.260510","title":"A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Bigram; Naive Bayes classifier; Artificial intelligence; Machine learning; Support vector machine; Sentence; Supervised learning; Perceptron; Natural language processing; Online learning; Term (time); Margin (machine learning); Artificial neural network; Trigram; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002145275,0.0006586623,0.0007140137,0.001861461,0.0004830074,0.000842131,0.0004857585,0.0004984394,0.001372548],"category_scores_gemma":[0.005260123,0.0001931642,0.0008909285,0.0008936597,0.000225577,0.0008146837,0.0004084331,0.0005167121,0.0008576767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005503739,"about_ca_system_score_gemma":0.0006356933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002260295,"about_ca_topic_score_gemma":0.002134994,"domain_scores_codex":[0.9983457,0.0005087672,0.0001920357,0.0003424717,0.0005028585,0.0001081199],"domain_scores_gemma":[0.9973801,0.000873415,0.0002836668,0.0001660124,0.001233268,0.00006349279],"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":[0.0008003702,0.0008117768,0.04879656,0.0003041572,0.0004661451,0.0002431183,0.0004379473,0.06741857,0.03107557,0.002116755,0.008140204,0.8393887],"study_design_scores_gemma":[0.0000159016,0.0001368093,0.01322834,0.00001283458,0.00003577055,0.00008889117,0.00008183028,0.9786167,0.005844701,0.0008808006,0.001040932,0.00001658253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4251103,0.0004945957,0.5656173,0.0003970404,0.0002145513,0.0004685582,0.001002562,0.002271016,0.004424107],"genre_scores_gemma":[0.8424404,0.0001398653,0.1523228,0.00006589299,0.0001421433,0.0002061631,0.001744916,0.00007047786,0.002867389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002260295,"threshold_uncertainty_score":0.01134545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590904730291073,"score_gpt":0.2891098331423615,"score_spread":0.2632007858394508,"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."}}