{"id":"W4286703699","doi":"10.4018/978-1-6684-6303-1.ch052","title":"Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques","year":2022,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reputation; Sentiment analysis; Computer science; Product (mathematics); E-commerce; Reputation system; Domain (mathematical analysis); Machine learning; Artificial intelligence; Data science; World Wide Web; 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":[0.0008679599,0.0007816073,0.000700796,0.000940899,0.0003860242,0.001209773,0.0007084521,0.0008246762,0.002053261],"category_scores_gemma":[0.001584827,0.0003368859,0.000921701,0.0007180459,0.0001916068,0.001144568,0.0003438309,0.000773919,0.001789499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006273044,"about_ca_system_score_gemma":0.0004947803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004414979,"about_ca_topic_score_gemma":0.002924889,"domain_scores_codex":[0.9996454,0.00009731101,0.00002983807,0.00007711881,0.0001047833,0.00004567056],"domain_scores_gemma":[0.9993772,0.0002066212,0.00006751105,0.00005012356,0.0002756328,0.00002290674],"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":[0.0003634582,0.0004362101,0.01594713,0.0002548799,0.0002721596,0.0004444362,0.0002165507,0.4584189,0.03002365,0.007670555,0.01260923,0.4733428],"study_design_scores_gemma":[0.000003015237,0.00001858299,0.0004805779,0.000002602854,0.00001046896,0.00001886976,0.000008030309,0.9973522,0.001117084,0.0005868904,0.0003978369,0.000003794038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06241414,0.0003597407,0.9299495,0.0002784318,0.0001045319,0.0001104842,0.0001940759,0.002019962,0.00456918],"genre_scores_gemma":[0.6621673,0.0004826633,0.3299282,0.0001522949,0.000154312,0.0001713281,0.0008221912,0.0001552124,0.005966593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004414979,"threshold_uncertainty_score":0.008778572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120983389308109,"score_gpt":0.2527722334794401,"score_spread":0.231562399586359,"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."}}