{"id":"W1589062066","doi":"10.1109/icws.2015.30","title":"Extracting, Ranking, and Evaluating Quality Features of Web Services through User Review Sentiment Analysis","year":2015,"lang":"en","type":"article","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Ranking (information retrieval); Quality (philosophy); Sentiment analysis; Information retrieval; World Wide Web; Data mining; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003026472,0.0006472736,0.0009057088,0.004159223,0.0004633371,0.001313171,0.0003043273,0.0002867346,0.0005014579],"category_scores_gemma":[0.01159237,0.0001435951,0.0006795336,0.002160869,0.0002191077,0.001033353,0.00038795,0.0003547154,0.0003834213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006574077,"about_ca_system_score_gemma":0.0007903313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003909049,"about_ca_topic_score_gemma":0.005978799,"domain_scores_codex":[0.9963554,0.001061386,0.0003629729,0.0003256412,0.001744714,0.0001498994],"domain_scores_gemma":[0.9890098,0.003321457,0.001532551,0.0003648173,0.005537204,0.000234108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009863281,0.0005179544,0.1390203,0.001603922,0.0004812686,0.0007262311,0.001964876,0.006078105,0.07215444,0.001027946,0.0134184,0.7620203],"study_design_scores_gemma":[0.00009461302,0.001741732,0.4426762,0.0003032654,0.000943107,0.001103447,0.004253972,0.446914,0.07175054,0.002126173,0.02785316,0.0002398429],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9157481,0.003439676,0.06960627,0.0008429578,0.0001752998,0.0008634925,0.001967793,0.001011922,0.006344526],"genre_scores_gemma":[0.9414594,0.001120832,0.05336251,0.00009464057,0.0001387542,0.0002431706,0.001732757,0.00005745701,0.001790484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004159223,"threshold_uncertainty_score":0.01600569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04974594866229595,"score_gpt":0.3753971554552797,"score_spread":0.3256512067929838,"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."}}