{"id":"W294906217","doi":"","title":"Helpful or Unhelpful: A Linear Approach for Ranking Product Reviews","year":2010,"lang":"en","type":"article","venue":"Journal of electronic commerce research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Product (mathematics); Ranking (information retrieval); Purchasing; The Internet; World Wide Web; Information retrieval; Data science; Marketing; Psychology; Business; Mathematics","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":[0.004620948,0.001344926,0.001183477,0.004856228,0.0008198696,0.001843202,0.002084465,0.001572746,0.003282242],"category_scores_gemma":[0.01186403,0.0006334136,0.001091741,0.002823291,0.0007771964,0.001766113,0.0006978774,0.001640253,0.002007434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001819525,"about_ca_system_score_gemma":0.001480592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01771906,"about_ca_topic_score_gemma":0.02127812,"domain_scores_codex":[0.9955549,0.002110984,0.0003186109,0.0008864004,0.0008747369,0.0002543111],"domain_scores_gemma":[0.9854107,0.01037092,0.00105971,0.0003457365,0.002503866,0.0003090866],"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":[0.001002477,0.001215233,0.05716355,0.0006788899,0.0007847705,0.0004972955,0.0007006329,0.3494574,0.005365079,0.008439318,0.01480069,0.5598947],"study_design_scores_gemma":[0.0000118134,0.0001291495,0.001819837,0.00001785369,0.00003602024,0.0000676634,0.00003050995,0.9954627,0.0003395464,0.001609326,0.0004526651,0.00002280677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1459832,0.00300499,0.8388277,0.00257966,0.000229166,0.000505183,0.001090139,0.002850994,0.004928892],"genre_scores_gemma":[0.838008,0.0005591151,0.1532171,0.0004819547,0.000364697,0.0003751206,0.0009324821,0.00008390482,0.005977648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01771906,"threshold_uncertainty_score":0.03523183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1643859954326166,"score_gpt":0.4424971498965129,"score_spread":0.2781111544638963,"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."}}