{"id":"W1562192992","doi":"10.1007/978-3-642-01187-0_1","title":"Helping E-Commerce Consumers Make Good Purchase Decisions: A User Reviews-Based Approach","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Product (mathematics); Order (exchange); Computer science; E-commerce; Ranking (information retrieval); Task (project management); Reading (process); Advertising; Marketing; World Wide Web; Business; Information retrieval; Engineering; Psychology","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":[0.002964685,0.0009168879,0.001165322,0.002317998,0.0007052846,0.002793772,0.0008941534,0.001529057,0.002146952],"category_scores_gemma":[0.010847,0.0004886898,0.0005838347,0.001602946,0.0003379764,0.002885413,0.0006094054,0.0008494889,0.001238377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006421036,"about_ca_system_score_gemma":0.0008611894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004111134,"about_ca_topic_score_gemma":0.01139446,"domain_scores_codex":[0.9974916,0.001052093,0.0001240773,0.0002485835,0.0009931795,0.00009059753],"domain_scores_gemma":[0.9904512,0.005325118,0.0006533518,0.0002284526,0.003144312,0.0001975641],"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":[0.001279463,0.002111475,0.03787784,0.001057232,0.0006700277,0.0003905012,0.001784897,0.008709916,0.01626421,0.003881353,0.04615247,0.8798207],"study_design_scores_gemma":[0.000339668,0.001840277,0.07649121,0.0003808185,0.002128778,0.001018409,0.005166574,0.8040656,0.03576637,0.01948531,0.05290933,0.0004075336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4808744,0.01335391,0.4023995,0.0136208,0.0004951443,0.001958642,0.00266739,0.003789728,0.08084043],"genre_scores_gemma":[0.8068523,0.001976475,0.1776219,0.0008062077,0.0003425335,0.0002697437,0.000943138,0.0001258517,0.01106181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004111134,"threshold_uncertainty_score":0.01567894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03528846520517465,"score_gpt":0.2737156629012034,"score_spread":0.2384271976960288,"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."}}